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Sample records for methodology unsupervised clustering

  1. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

    Science.gov (United States)

    Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

    2015-03-01

    Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new

  2. Factored Translation with Unsupervised Word Clusters

    DEFF Research Database (Denmark)

    Rishøj, Christian; Søgaard, Anders

    2011-01-01

    Unsupervised word clustering algorithms — which form word clusters based on a measure of distributional similarity — have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word...... clusters as factors in statistical machine translation. Although some of the language pairs in this work clearly benefit from the factor augmentation, there is no consistent improvement in translation accuracy across the board. For all language pairs, the word clusters clearly improve translation for some...... proportion of the sentences in the test set, but has a weak or even detrimental effect on the rest. It is shown that if one could determine whether or not to use a factor when translating a given sentence, rather substantial improvements in precision could be achieved for all of the language pairs evaluated...

  3. Clustervision: Visual Supervision of Unsupervised Clustering.

    Science.gov (United States)

    Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

    2018-01-01

    Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

  4. Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

    Directory of Open Access Journals (Sweden)

    Tushar H Jaware

    2013-10-01

    Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

  5. An Improved Unsupervised Modeling Methodology For Detecting Fraud In Vendor Payment Transactions

    National Research Council Canada - National Science Library

    Rouillard, Gregory

    2003-01-01

    ...) vendor payment transactions through Unsupervised Modeling (cluster analysis) . Clementine Data Mining software is used to construct unsupervised models of vendor payment data using the K-Means, Two Step, and Kohonen algorithms...

  6. Unsupervised Learning (Clustering) of Odontocete Echolocation Clicks

    Science.gov (United States)

    2015-09-30

    develop methods for clustering of marine mammal echolocation clicks to learn about species assemblages where little or no prior knowledge exists about... Mexico or the Atlanic. 2 APPROACH Acoustic encounters with odontocetes are detected automatically and noise-corrected cepstral features...Estmation of Marine Mammals Using Passive Acoustic Monitoring (DCLDE). KL divergence maps were created for all known species, but the sperm whale

  7. Misty Mountain clustering: application to fast unsupervised flow cytometry gating

    Directory of Open Access Journals (Sweden)

    Sealfon Stuart C

    2010-10-01

    Full Text Available Abstract Background There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments. Results To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment. Conclusions

  8. Unsupervised color image segmentation using a lattice algebra clustering technique

    Science.gov (United States)

    Urcid, Gonzalo; Ritter, Gerhard X.

    2011-08-01

    In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.

  9. GibbsCluster: unsupervised clustering and alignment of peptide sequences

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Alvarez, Bruno; Nielsen, Morten

    2017-01-01

    motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large......-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0....

  10. Artificial immune kernel clustering network for unsupervised image segmentation

    Institute of Scientific and Technical Information of China (English)

    Wenlong Huang; Licheng Jiao

    2008-01-01

    An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

  11. Unsupervised active learning based on hierarchical graph-theoretic clustering.

    Science.gov (United States)

    Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

    2009-10-01

    Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

  12. Unsupervised Two-Way Clustering of Metagenomic Sequences

    Directory of Open Access Journals (Sweden)

    Shruthi Prabhakara

    2012-01-01

    Full Text Available A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.

  13. Application of cluster analysis and unsupervised learning to multivariate tissue characterization

    International Nuclear Information System (INIS)

    Momenan, R.; Insana, M.F.; Wagner, R.F.; Garra, B.S.; Loew, M.H.

    1987-01-01

    This paper describes a procedure for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characterization. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons two objectives are sought: a) How well does the clustering method group the data?; b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data

  14. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

    NARCIS (Netherlands)

    S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

    2000-01-01

    textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

  15. Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

    Science.gov (United States)

    Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

    2017-01-01

    Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

  16. Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

    Directory of Open Access Journals (Sweden)

    Jiayi Wu

    Full Text Available Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM. We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

  17. Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

    Science.gov (United States)

    Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

    2016-01-01

    In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

  18. Data mining with unsupervised clustering using photonic micro-ring resonators

    Science.gov (United States)

    McAulay, Alastair D.

    2013-09-01

    Data is commonly moved through optical fiber in modern data centers and may be stored optically. We propose an optical method of data mining for future data centers to enhance performance. For example, in clustering, a form of unsupervised learning, we propose that parameters corresponding to information in a database are converted from analog values to frequencies, as in the brain's neurons, where similar data will have close frequencies. We describe the Wilson-Cowan model for oscillating neurons. In optics we implement the frequencies with micro ring resonators. Due to the influence of weak coupling, a group of resonators will form clusters of similar frequencies that will indicate the desired parameters having close relations. Fewer clusters are formed as clustering proceeds, which allows the creation of a tree showing topics of importance and their relationships in the database. The tree can be used for instance to target advertising and for planning.

  19. Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

    Directory of Open Access Journals (Sweden)

    Yubo Jiao

    2013-01-01

    Full Text Available Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

  20. A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Xite Wang

    2017-01-01

    Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

  1. Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

    Science.gov (United States)

    Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

    2014-09-30

    This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

    Directory of Open Access Journals (Sweden)

    Nour-Eddine El Harchaoui

    2013-01-01

    Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

  3. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

    Directory of Open Access Journals (Sweden)

    Pradipta Maji

    Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

  4. On the Multi-Modal Object Tracking and Image Fusion Using Unsupervised Deep Learning Methodologies

    Science.gov (United States)

    LaHaye, N.; Ott, J.; Garay, M. J.; El-Askary, H. M.; Linstead, E.

    2017-12-01

    The number of different modalities of remote-sensors has been on the rise, resulting in large datasets with different complexity levels. Such complex datasets can provide valuable information separately, yet there is a bigger value in having a comprehensive view of them combined. As such, hidden information can be deduced through applying data mining techniques on the fused data. The curse of dimensionality of such fused data, due to the potentially vast dimension space, hinders our ability to have deep understanding of them. This is because each dataset requires a user to have instrument-specific and dataset-specific knowledge for optimum and meaningful usage. Once a user decides to use multiple datasets together, deeper understanding of translating and combining these datasets in a correct and effective manner is needed. Although there exists data centric techniques, generic automated methodologies that can potentially solve this problem completely don't exist. Here we are developing a system that aims to gain a detailed understanding of different data modalities. Such system will provide an analysis environment that gives the user useful feedback and can aid in research tasks. In our current work, we show the initial outputs our system implementation that leverages unsupervised deep learning techniques so not to burden the user with the task of labeling input data, while still allowing for a detailed machine understanding of the data. Our goal is to be able to track objects, like cloud systems or aerosols, across different image-like data-modalities. The proposed system is flexible, scalable and robust to understand complex likenesses within multi-modal data in a similar spatio-temporal range, and also to be able to co-register and fuse these images when needed.

  5. Classification and unsupervised clustering of LIGO data with Deep Transfer Learning

    Science.gov (United States)

    George, Daniel; Shen, Hongyu; Huerta, E. A.

    2018-05-01

    Gravitational wave detection requires a detailed understanding of the response of the LIGO and Virgo detectors to true signals in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian transients, such as glitches, since their occurrence rate in LIGO and Virgo data can obscure or even mimic true gravitational wave signals. Therefore, successfully identifying and excising these anomalies from gravitational wave data is of utmost importance for the detection and characterization of true signals and for the accurate computation of their significance. To facilitate this work, we present the first application of deep learning combined with transfer learning to show that knowledge from pretrained models for real-world object recognition can be transferred for classifying spectrograms of glitches. To showcase this new method, we use a data set of twenty-two classes of glitches, curated and labeled by the Gravity Spy project using data collected during LIGO's first discovery campaign. We demonstrate that our Deep Transfer Learning method enables an optimal use of very deep convolutional neural networks for glitch classification given small and unbalanced training data sets, significantly reduces the training time, and achieves state-of-the-art accuracy above 98.8%, lowering the previous error rate by over 60%. More importantly, once trained via transfer learning on the known classes, we show that our neural networks can be truncated and used as feature extractors for unsupervised clustering to automatically group together new unknown classes of glitches and anomalous signals. This novel capability is of paramount importance to identify and remove new types of glitches which will occur as the LIGO/Virgo detectors gradually attain design sensitivity.

  6. AUTOMATED UNSUPERVISED CLASSIFICATION OF THE SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA USING k-MEANS CLUSTERING

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez Almeida, J.; Allende Prieto, C., E-mail: jos@iac.es, E-mail: callende@iac.es [Instituto de Astrofisica de Canarias, E-38205 La Laguna, Tenerife (Spain)

    2013-01-20

    Large spectroscopic surveys require automated methods of analysis. This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior physical framework. We work with a representative set of stellar spectra associated with the Sloan Digital Sky Survey (SDSS) SEGUE and SEGUE-2 programs, which consists of 173,390 spectra from 3800 to 9200 A sampled on 3849 wavelengths. We classify the original spectra as well as the spectra with the continuum removed. The second set only contains spectral lines, and it is less dependent on uncertainties of the flux calibration. The classification of the spectra with continuum renders 16 major classes. Roughly speaking, stars are split according to their colors, with enough finesse to distinguish dwarfs from giants of the same effective temperature, but with difficulties to separate stars with different metallicities. There are classes corresponding to particular MK types, intrinsically blue stars, dust-reddened, stellar systems, and also classes collecting faulty spectra. Overall, there is no one-to-one correspondence between the classes we derive and the MK types. The classification of spectra without continuum renders 13 classes, the color separation is not so sharp, but it distinguishes stars of the same effective temperature and different metallicities. Some classes thus obtained present a fairly small range of physical parameters (200 K in effective temperature, 0.25 dex in surface gravity, and 0.35 dex in metallicity), so that the classification can be used to estimate the main physical parameters of some stars at a minimum computational cost. We also analyze the outliers of the classification. Most of them turn out to be failures of the reduction pipeline, but there are also high redshift QSOs, multiple stellar systems, dust-reddened stars, galaxies, and, finally, odd

  7. flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.

    Science.gov (United States)

    Ge, Yongchao; Sealfon, Stuart C

    2012-08-01

    For flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. The former is computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot be applied directly to high-dimensional data as the computational time and memory become unmanageable and the estimated histogram is unreliable. An algorithm without these two problems would be very useful. In this article, we combine ideas from the finite mixture model and histogram spatial exploration. This new algorithm, which we call flowPeaks, can be applied directly to high-dimensional data and identify irregular shape clusters. The algorithm first uses K-means algorithm with a large K to partition the cell population into many small clusters. These partitioned data allow the generation of a smoothed density function using the finite mixture model. All local peaks are exhaustively searched by exploring the density function and the cells are clustered by the associated local peak. The algorithm flowPeaks is automatic, fast and reliable and robust to cluster shape and outliers. This algorithm has been applied to flow cytometry data and it has been compared with state of the art algorithms, including Misty Mountain, FLOCK, flowMeans, flowMerge and FLAME. The R package flowPeaks is available at https://github.com/yongchao/flowPeaks. yongchao.ge@mssm.edu Supplementary data are available at Bioinformatics online.

  8. ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

    Science.gov (United States)

    Oluwadare, Oluwatosin; Cheng, Jianlin

    2017-11-14

    With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts. The identification of the TADs for a genome is useful for studying gene regulation, genomic interaction, and genome function. Here, we formulate the TAD identification problem as an unsupervised machine learning (clustering) problem, and develop a new TAD identification method called ClusterTAD. We introduce a novel method to represent chromosomal contacts as features to be used by the clustering algorithm. Our results show that ClusterTAD can accurately predict the TADs on a simulated Hi-C data. Our method is also largely complementary and consistent with existing methods on the real Hi-C datasets of two mouse cells. The validation with the chromatin immunoprecipitation (ChIP) sequencing (ChIP-Seq) data shows that the domain boundaries identified by ClusterTAD have a high enrichment of CTCF binding sites, promoter-related marks, and enhancer-related histone modifications. As ClusterTAD is based on a proven clustering approach, it opens a new avenue to apply a large array of clustering methods developed in the machine learning field to the TAD identification problem. The source code, the results, and the TADs generated for the simulated and real Hi-C datasets are available here: https://github.com/BDM-Lab/ClusterTAD .

  9. An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

    Science.gov (United States)

    Wendel, Jochen; Buttenfield, Barbara P.; Stanislawski, Larry V.

    2016-01-01

    Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

  10. A Hierarchical Clustering Methodology for the Estimation of Toxicity

    Science.gov (United States)

    A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

  11. Unsupervised Feature Subset Selection

    DEFF Research Database (Denmark)

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  12. Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

    Science.gov (United States)

    LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

    2001-01-01

    The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

  13. Hadoop Cluster Deployment: A Methodological Approach

    Directory of Open Access Journals (Sweden)

    Ronaldo Celso Messias Correia

    2018-05-01

    Full Text Available For a long time, data has been treated as a general problem because it just represents fractions of an event without any relevant purpose. However, the last decade has been just about information and how to get it. Seeking meaning in data and trying to solve scalability problems, many frameworks have been developed to improve data storage and its analysis. As a framework, Hadoop was presented as a powerful tool to deal with large amounts of data. However, it still causes doubts about how to deal with its deployment and if there is any reliable method to compare the performance of distinct Hadoop clusters. This paper presents a methodology based on benchmark analysis to guide the Hadoop cluster deployment. The experiments employed The Apache Hadoop and the Hadoop distributions of Cloudera, Hortonworks, and MapR, analyzing the architectures on local and on clouding—using centralized and geographically distributed servers. The results show the methodology can be dynamically applied on a reliable comparison among different architectures. Additionally, the study suggests that the knowledge acquired can be used to improve the data analysis process by understanding the Hadoop architecture.

  14. Unsupervised learning algorithms

    CERN Document Server

    Aydin, Kemal

    2016-01-01

    This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...

  15. High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

    Directory of Open Access Journals (Sweden)

    Dieter Hendricks

    2016-02-01

    Full Text Available We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

  16. Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

    Science.gov (United States)

    Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

    2017-12-01

    To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

  17. Automated assessment and tracking of human body thermal variations using unsupervised clustering.

    Science.gov (United States)

    Yousefi, Bardia; Fleuret, Julien; Zhang, Hai; Maldague, Xavier P V; Watt, Raymond; Klein, Matthieu

    2016-12-01

    The presented approach addresses a review of the overheating that occurs during radiological examinations, such as magnetic resonance imaging, and a series of thermal experiments to determine a thermally suitable fabric material that should be used for radiological gowns. Moreover, an automatic system for detecting and tracking of the thermal fluctuation is presented. It applies hue-saturated-value-based kernelled k-means clustering, which initializes and controls the points that lie on the region-of-interest (ROI) boundary. Afterward, a particle filter tracks the targeted ROI during the video sequence independently of previous locations of overheating spots. The proposed approach was tested during experiments and under conditions very similar to those used during real radiology exams. Six subjects have voluntarily participated in these experiments. To simulate the hot spots occurring during radiology, a controllable heat source was utilized near the subject's body. The results indicate promising accuracy for the proposed approach to track hot spots. Some approximations were used regarding the transmittance of the atmosphere, and emissivity of the fabric could be neglected because of the independence of the proposed approach for these parameters. The approach can track the heating spots continuously and correctly, even for moving subjects, and provides considerable robustness against motion artifact, which occurs during most medical radiology procedures.

  18. The Typology of Methodological Approaches to Development of Innovative Clusters

    Directory of Open Access Journals (Sweden)

    Farat Olexandra V.

    2017-06-01

    Full Text Available The aim of the article is to study the existing methodological approaches to assessing the development of enterprises for further substantiation of possibilities of their using by cluster associations. As a result of research, based on the analysis of scientific literature, the most applicable methodological approaches to assessing the development of enterprises are characterized. 8 methodical approaches to assessing the level of development of enterprises and 4 methodological approaches to assessing the level of development of clusters are singled out. Each of the approaches is characterized by the presence of certain advantages and disadvantages, but none of them allows to obtain a systematic assessment of all areas of cluster functioning, identify possible reserves for cluster competitiveness growth and characterize possible strategies for their future development. Taking into account peculiarities of the functioning and development of cluster associations of enterprises, we propose our own methodological approach for assessing the development of innovative cluster structures.

  19. An Intelligent Clustering Based Methodology for Confusable ...

    African Journals Online (AJOL)

    Journal of the Nigerian Association of Mathematical Physics ... The system assigns patients with severity levels in all the clusters. ... The system compares favorably with diagnosis arrived at by experienced physicians and also provides patients' level of severity in each confusable disease and the degree of confusability of ...

  20. Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

    Science.gov (United States)

    Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

    2017-01-31

    Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

  1. Methodologies for Improved Tag Cloud Generation with Clustering

    DEFF Research Database (Denmark)

    Leginus, Martin; Dolog, Peter; Lage, Ricardo Gomes

    2012-01-01

    Tag clouds are useful means for navigation in the social web systems. Usually the systems implement the tag cloud generation based on tag popularity which is not always the best method. In this paper we propose methodologies on how to combine clustering into the tag cloud generation to improve...... coverage and overlap. We study several clustering algorithms to generate tag clouds. We show that by extending cloud generation based on tag popularity with clustering we slightly improve coverage. We also show that if the cloud is generated by clustering independently of the tag popularity baseline we...

  2. Conjunctive Conceptual Clustering: A Methodology and Experimentation.

    Science.gov (United States)

    1987-09-01

    observing a typical restaurant table on vhich there are such objects as food on a plate, a salad, utensils, salt and pepper, napkins , a ase with flowers, a...colored graph has nodes and inks that match only if they have corre-ponding link-olor and node-color labelg 4w 80 [SEtexture sa lif ba p S M i e d If...LINK LINK LINK LINK LINK 9 0 1 OPENdRECT RECTLOD 1 2 CL 10 0 0 LINK LINK INK LINK LINK ,~ . 0.5. Input file for attribute-based clustering The

  3. Unsupervised Learning and Generalization

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Larsen, Jan

    1996-01-01

    The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy ...... with supervised learning. The empirical and analytical estimates are compared for principal component analysis and for K-means clustering based density estimation......The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy...

  4. Unsupervised versus Supervised Identification of Prognostic Factors in Patients with Localized Retroperitoneal Sarcoma: A Data Clustering and Mahalanobis Distance Approach

    Directory of Open Access Journals (Sweden)

    Rita De Sanctis

    2018-01-01

    Full Text Available The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS patients by univariate and multivariate statistical techniques. A phase I-II study on localized RPS treated with high-dose ifosfamide and radiotherapy followed by surgery (ISG-STS 0303 protocol demonstrated that chemo/radiotherapy was safe and increased the 3-year relapse-free survival (RFS with respect to historical controls. Of 70 patients, twenty-six developed local, 10 distant, and 5 combined relapse. Median disease-free interval (DFI was 29.47 months. According to a discriminant function analysis, DFI, histology, relapse pattern, and the first treatment approach at relapse had a statistically significant prognostic impact. Based on scientific literature and clinical expertise, clinicopathological data were analyzed using both a supervised and an unsupervised classification method to predict the prognosis, with similar sample sizes (66 and 65, resp., in casewise approach and 70 in mean-substitution one. This is the first attempt to predict patients’ prognosis by means of multivariate statistics, and in this light, it looks noticable that (i some clinical data have a well-defined prognostic value, (ii the unsupervised model produced comparable results with respect to the supervised one, and (iii the appropriate combination of both models appears fruitful and easily extensible to different clinical contexts.

  5. The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

    Science.gov (United States)

    Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

    2014-05-01

    Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the

  6. Humanitarian Logistics: a Clustering Methodology for Assisting Humanitarian Operations

    Directory of Open Access Journals (Sweden)

    Fabiana santos Lima

    2014-06-01

    Full Text Available In this paper, we propose a methodology to identify and classify regions by the type and frequency of disasters. The data on the clusters allow you to extract information that can be used in the preparedness phase as well as to identify the relief items needed to meet each cluster. Using this approach, the clusters are formed by using a computing tool that uses as the input the history data of the disasters in the Brazilian state of Santa Catarina, with a specific focus on: windstorms, hail, floods, droughts, landslides, and flash floods. The results show that the knowledge provided by the clustering analysis contributes to the decision making process in the response phase of Humanitarian Logistics (HL.

  7. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Yuchou Chang

    2008-02-01

    Full Text Available Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  8. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Hong Yi

    2008-01-01

    Full Text Available Abstract Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  9. Decomposition methods for unsupervised learning

    DEFF Research Database (Denmark)

    Mørup, Morten

    2008-01-01

    This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

  10. Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering

    International Nuclear Information System (INIS)

    Su, Kuan-Hao; Hu, Lingzhi; Traughber, Melanie; Stehning, Christian; Helle, Michael; Qian, Pengjiang; Thompson, Cheryl L.; Pereira, Gisele C.; Traughber, Bryan J.; Jordan, David W.; Herrmann, Karin A.; Muzic, Raymond F.

    2015-01-01

    Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2 ∗ images (1/T2 ∗ ) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2 ∗ , were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT

  11. Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data

    Energy Technology Data Exchange (ETDEWEB)

    Getman, Dan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Lopez, Anthony [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Dyson, Mark [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2015-09-01

    In this report, we introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.

  12. Unsupervised classification of variable stars

    Science.gov (United States)

    Valenzuela, Lucas; Pichara, Karim

    2018-03-01

    During the past 10 years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric data sets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data come from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query-based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire data set of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

  13. Classification of behavior using unsupervised temporal neural networks

    International Nuclear Information System (INIS)

    Adair, K.L.

    1998-03-01

    Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem

  14. Regional health care planning: a methodology to cluster facilities using community utilization patterns.

    Science.gov (United States)

    Delamater, Paul L; Shortridge, Ashton M; Messina, Joseph P

    2013-08-22

    Community-based health care planning and regulation necessitates grouping facilities and areal units into regions of similar health care use. Limited research has explored the methodologies used in creating these regions. We offer a new methodology that clusters facilities based on similarities in patient utilization patterns and geographic location. Our case study focused on Hospital Groups in Michigan, the allocation units used for predicting future inpatient hospital bed demand in the state's Bed Need Methodology. The scientific, practical, and political concerns that were considered throughout the formulation and development of the methodology are detailed. The clustering methodology employs a 2-step K-means + Ward's clustering algorithm to group hospitals. The final number of clusters is selected using a heuristic that integrates both a statistical-based measure of cluster fit and characteristics of the resulting Hospital Groups. Using recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan. Despite being developed within the politically charged climate of Certificate of Need regulation, we have provided an objective, replicable, and sustainable methodology to create Hospital Groups. Because the methodology is built upon theoretically sound principles of clustering analysis and health care service utilization, it is highly transferable across applications and suitable for grouping facilities or areal units.

  15. Unsupervised land cover change detection: meaningful sequential time series analysis

    CSIR Research Space (South Africa)

    Salmon, BP

    2011-06-01

    Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

  16. An intelligent clustering based methodology for confusable diseases ...

    African Journals Online (AJOL)

    Journal of Computer Science and Its Application ... In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for ... Data on patients diagnosed and confirmed by laboratory tests of viral ...

  17. WESTERN CHARPATHIAN RURAL MOUNTAIN TOURISM MAPPING THROUGH CLUSTER METHODOLOGY

    Directory of Open Access Journals (Sweden)

    Elena TOMA

    2013-10-01

    Full Text Available Rural tourism from Western Carpathian Mountain was characterized in the last years by a low occupancy rate and a decline in tourist arrivals, due, beside of the direct effects of economic crises, to the remote location of mountain villages and to the low quality of infrastructure. For this reason we consider that the implementation of complex and integrated products based on tour thematic circuits represents a real opportunity to develop local rural tourism industry. The aim of this paper is to identify which is the best networking solution, based on clustering analysis. The Multidimensional Scaling Method and Hierarchical Cluster Method permitted us to demonstrate and identify the best way of clustering, and, in this way, the best route for a potential tour touristic circuit. Reported to the counties from which the villages take part, the identified cluster concentrate 57.7% of rural touristic accommodations and 65.0% of tourist arrivals, but it has an occupancy rate of only 5.9%. By implementing new complex touristic products we consider that can be assured a rise of this touristic dimension of the cluster and we propose more in depth studies regarding the profile of the potential customers.

  18. Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

    Science.gov (United States)

    Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

    2017-08-01

    Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results

  19. Unsupervised Image Segmentation

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Mikeš, Stanislav

    2014-01-01

    Roč. 36, č. 4 (2014), s. 23-23 R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : unsupervised image segmentation Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2014/RO/haindl-0434412.pdf

  20. A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment

    Directory of Open Access Journals (Sweden)

    Simone Pascucci

    2018-04-01

    Full Text Available The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy. Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI the Normalized Difference Vegetation Index (NDVI, then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1 standard K-means; (2 functional K-means; (3 multivariate functional principal components clustering analysis; (4 hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of

  1. Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

    Energy Technology Data Exchange (ETDEWEB)

    Leinsinger, Gerda; Schlossbauer, Thomas; Scherr, Michael; Lange, Oliver; Reiser, Maximilian; Wismueller, Axel [Institute for Clinical Radiology University of Munich, Munich (Germany)

    2006-05-15

    We examined whether neural network clustering could support the characterization of diagnostically challenging breast lesions in dynamic magnetic resonance imaging (MRI). We examined 88 patients with 92 breast lesions (51 malignant, 41 benign). Lesions were detected by mammography and classified Breast Imaging and Reporting Data System (BIRADS) III (median diameter 14 mm). MRI was performed with a dynamic T1-weighted gradient echo sequence (one precontrast and five postcontrast series). Lesions with an initial contrast enhancement {>=}50% were selected with semiautomatic segmentation. For conventional analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were divided into four clusters using minimal-free-energy vector quantization (VQ). With conventional analysis, maximum accuracy in detecting breast cancer was 71%. With VQ, a maximum accuracy of 75% was observed. The slight improvement using VQ was mainly achieved by an increase of sensitivity, especially in invasive lobular carcinoma and ductal carcinoma in situ (DCIS). For lesion size, a high correlation between different observers was found (R{sup 2} = 0.98). VQ slightly improved the discrimination between malignant and benign indeterminate lesions (BIRADS III) in comparison with a standard evaluation method. (orig.)

  2. Framework methodology for increased energy efficiency and renewable feedstock integration in industrial clusters

    International Nuclear Information System (INIS)

    Hackl, Roman; Harvey, Simon

    2013-01-01

    Highlights: • Framework methodology for energy efficiency of process plants and total sites. • Identification of suitable biorefinery based on host site future energy systems. • Case study results show large energy savings of site wide heat integration. • Case study on refrigeration systems: 15% shaft work savings potential. • Case study on biorefinery integration: utility savings potential of up to 37%. - Abstract: Energy intensive industries, such as the bulk chemical industry, are facing major challenges and adopting strategies to face these challenges. This paper investigates options for clusters of chemical process plants to decrease their energy and emission footprints. There is a wide range of technologies and process integration opportunities available for achieving these objectives, including (i) decreasing fossil fuel and electricity demand by increasing heat integration within individual processes and across the total cluster site; (ii) replacing fossil feedstocks with renewables and biorefinery integration with the existing cluster; (iii) increasing external utilization of excess process heat wherever possible. This paper presents an overview of the use of process integration methods for development of chemical clusters. Process simulation, pinch analysis, Total Site Analysis (TSA) and exergy concepts are combined in a holistic approach to identify opportunities to improve energy efficiency and integrate renewable feedstocks within such clusters. The methodology is illustrated by application to a chemical cluster in Stenungsund on the West Coast of Sweden consisting of five different companies operating six process plants. The paper emphasizes and quantifies the gains that can be made by adopting a total site approach for targeting energy efficiency measures within the cluster and when investigating integration opportunities for advanced biorefinery concepts compared to restricting the analysis to the individual constituent plants. The

  3. Unit commitment solution using agglomerative and divisive cluster algorithm : an effective new methodology

    Energy Technology Data Exchange (ETDEWEB)

    Reddy, N.M.; Reddy, K.R. [G. Narayanamma Inst. of Technology and Science, Hyderabad (India). Dept. of Electrical Engineering; Ramana, N.V. [JNTU College of Engineering, Jagityala (India). Dept. of Electrical Engineering

    2008-07-01

    Thermal power plants consist of several generating units with different generating capacities, fuel cost per MWH generated, minimum up/down times, and start-up or shut-down costs. The Unit Commitment (UC) problem in power systems involves determining the start-up and shut-down schedules of thermal generating units to meet forecasted load over a future short term for a period of one to seven days. This paper presented a new approach for the most complex UC problem using agglomerative and divisive hierarchical clustering. Euclidean costs, which are a measure of differences in fuel cost and start-up costs of any two units, were first calculated. Then, depending on the value of Euclidean costs, similar type of units were placed in a cluster. The proposed methodology has 2 individual algorithms. An agglomerative cluster algorithm is used while the load is increasing, and a divisive cluster algorithm is used when the load is decreasing. A search was conducted for an optimal solution for a minimal number of clusters and cluster data points. A standard ten-unit thermal unit power system was used to test and evaluate the performance of the method for a period of 24 hours. The new approach proved to be quite effective and satisfactory. 15 refs., 9 tabs., 5 figs.

  4. Extending the input–output energy balance methodology in agriculture through cluster analysis

    International Nuclear Information System (INIS)

    Bojacá, Carlos Ricardo; Casilimas, Héctor Albeiro; Gil, Rodrigo; Schrevens, Eddie

    2012-01-01

    The input–output balance methodology has been applied to characterize the energy balance of agricultural systems. This study proposes to extend this methodology with the inclusion of multivariate analysis to reveal particular patterns in the energy use of a system. The objective was to demonstrate the usefulness of multivariate exploratory techniques to analyze the variability found in a farming system and, establish efficiency categories that can be used to improve the energy balance of the system. To this purpose an input–output analysis was applied to the major greenhouse tomato production area in Colombia. Individual energy profiles were built and the k-means clustering method was applied to the production factors. On average, the production system in the study zone consumes 141.8 GJ ha −1 to produce 96.4 GJ ha −1 , resulting in an energy efficiency of 0.68. With the k-means clustering analysis, three clusters of farmers were identified with energy efficiencies of 0.54, 0.67 and 0.78. The most energy efficient cluster grouped 56.3% of the farmers. It is possible to optimize the production system by improving the management practices of those with the lowest energy use efficiencies. Multivariate analysis techniques demonstrated to be a complementary pathway to improve the energy efficiency of a system. -- Highlights: ► An input–output energy balance was estimated for greenhouse tomatoes in Colombia. ► We used the k-means clustering method to classify growers based on their energy use. ► Three clusters of growers were found with energy efficiencies of 0.54, 0.67 and 0.78. ► Overall system optimization is possible by improving the energy use of the less efficient.

  5. Content Discovery from Composite Audio : An unsupervised approach

    NARCIS (Netherlands)

    Lu, L.

    2009-01-01

    In this thesis, we developed and assessed a novel robust and unsupervised framework for semantic inference from composite audio signals. We focused on the problem of detecting audio scenes and grouping them into meaningful clusters. Our approach addressed all major steps in a general process of

  6. Tune Your Brown Clustering, Please

    DEFF Research Database (Denmark)

    Derczynski, Leon; Chester, Sean; Bøgh, Kenneth Sejdenfaden

    2015-01-01

    Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly...

  7. Unsupervised Classification Using Immune Algorithm

    OpenAIRE

    Al-Muallim, M. T.; El-Kouatly, R.

    2012-01-01

    Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...

  8. Wind Energy Development in India and a Methodology for Evaluating Performance of Wind Farm Clusters

    Directory of Open Access Journals (Sweden)

    Sanjeev H. Kulkarni

    2016-01-01

    Full Text Available With maturity of advanced technologies and urgent requirement for maintaining a healthy environment with reasonable price, India is moving towards a trend of generating electricity from renewable resources. Wind energy production, with its relatively safer and positive environmental characteristics, has evolved from a marginal activity into a multibillion dollar industry today. Wind energy power plants, also known as wind farms, comprise multiple wind turbines. Though there are several wind-mill clusters producing energy in different geographical locations across the world, evaluating their performance is a complex task and is an important focus for stakeholders. In this work an attempt is made to estimate the performance of wind clusters employing a multicriteria approach. Multiple factors that affect wind farm operations are analyzed by taking experts opinions, and a performance ranking of the wind farms is generated. The weights of the selection criteria are determined by pairwise comparison matrices of the Analytic Hierarchy Process (AHP. The proposed methodology evaluates wind farm performance based on technical, economic, environmental, and sociological indicators. Both qualitative and quantitative parameters were considered. Empirical data were collected through questionnaire from the selected wind farms of Belagavi district in the Indian State of Karnataka. This proposed methodology is a useful tool for cluster analysis.

  9. Unsupervised Language Acquisition

    Science.gov (United States)

    de Marcken, Carl

    1996-11-01

    This thesis presents a computational theory of unsupervised language acquisition, precisely defining procedures for learning language from ordinary spoken or written utterances, with no explicit help from a teacher. The theory is based heavily on concepts borrowed from machine learning and statistical estimation. In particular, learning takes place by fitting a stochastic, generative model of language to the evidence. Much of the thesis is devoted to explaining conditions that must hold for this general learning strategy to arrive at linguistically desirable grammars. The thesis introduces a variety of technical innovations, among them a common representation for evidence and grammars, and a learning strategy that separates the ``content'' of linguistic parameters from their representation. Algorithms based on it suffer from few of the search problems that have plagued other computational approaches to language acquisition. The theory has been tested on problems of learning vocabularies and grammars from unsegmented text and continuous speech, and mappings between sound and representations of meaning. It performs extremely well on various objective criteria, acquiring knowledge that causes it to assign almost exactly the same structure to utterances as humans do. This work has application to data compression, language modeling, speech recognition, machine translation, information retrieval, and other tasks that rely on either structural or stochastic descriptions of language.

  10. Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects

    Directory of Open Access Journals (Sweden)

    Dreyhaupt, Jens

    2017-05-01

    Full Text Available An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”. Compared with studies with individual randomization, studies with cluster randomization normally require (significantly larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies.Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

  11. Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects.

    Science.gov (United States)

    Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer

    2017-01-01

    An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called "cluster randomization"). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

  12. Onto-clust--a methodology for combining clustering analysis and ontological methods for identifying groups of comorbidities for developmental disorders.

    Science.gov (United States)

    Peleg, Mor; Asbeh, Nuaman; Kuflik, Tsvi; Schertz, Mitchell

    2009-02-01

    Children with developmental disorders usually exhibit multiple developmental problems (comorbidities). Hence, such diagnosis needs to revolve on developmental disorder groups. Our objective is to systematically identify developmental disorder groups and represent them in an ontology. We developed a methodology that combines two methods (1) a literature-based ontology that we created, which represents developmental disorders and potential developmental disorder groups, and (2) clustering for detecting comorbid developmental disorders in patient data. The ontology is used to interpret and improve clustering results and the clustering results are used to validate the ontology and suggest directions for its development. We evaluated our methodology by applying it to data of 1175 patients from a child development clinic. We demonstrated that the ontology improves clustering results, bringing them closer to an expert generated gold-standard. We have shown that our methodology successfully combines an ontology with a clustering method to support systematic identification and representation of developmental disorder groups.

  13. Methodological Foundations of Clustering and Innovativeness for Establishing the Competitive Production of Biofuels

    Directory of Open Access Journals (Sweden)

    Klymchuk Oleksandr V.

    2016-05-01

    Full Text Available The article is aimed to study the worldwide trends in development of innovative processes and creation of cluster structures for elaborating methodological foundations for establishing the competitive production of biofuels. The article highlights the cluster approaches in conducting the global commercial activities that create effective mechanisms and tools to encourage innovation-investment regional development and can be characterized by their relevance for the Ukrainian economy. Emphasis is made on the matter that clustering is one of the key tools for structuring the energy market, integrated exploiting the potential of bioenergy industry sector, management of the economic policies of redistribution of value added, implementation of the growth of investment attractiveness of the biofuel industry in our country. It has been concluded that cluster development in the biofuel production will stimulate specialization and cooperation processes in the agro-industrial economy sector, bringing together related businesses in the direction of an effective interaction, thereby ensuring a high level of competitiveness of biofuels in both the national and the international markets.

  14. SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

    Energy Technology Data Exchange (ETDEWEB)

    Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong

  15. Supervised versus unsupervised categorization: two sides of the same coin?

    Science.gov (United States)

    Pothos, Emmanuel M; Edwards, Darren J; Perlman, Amotz

    2011-09-01

    Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions.

  16. A new methodology to study customer electrocardiogram using RFM analysis and clustering

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Gholamian

    2011-04-01

    Full Text Available One of the primary issues on marketing planning is to know the customer's behavioral trends. A customer's purchasing interest may fluctuate for different reasons and it is important to find the declining or increasing trends whenever they happen. It is important to study these fluctuations to improve customer relationships. There are different methods to increase the customer's willingness such as planning good promotions, an increase on advertisement, etc. This paper proposes a new methodology to measure customer's behavioral trends called customer electrocardiogram. The proposed model of this paper uses K-means clustering method with RFM analysis to study customer's fluctuations over different time frames. We also apply the proposed electrocardiogram methodology for a real-world case study of food industry and the results are discussed in details.

  17. Cluster Randomised Trials in Cochrane Reviews: Evaluation of Methodological and Reporting Practice.

    Directory of Open Access Journals (Sweden)

    Marty Richardson

    Full Text Available Systematic reviews can include cluster-randomised controlled trials (C-RCTs, which require different analysis compared with standard individual-randomised controlled trials. However, it is not known whether review authors follow the methodological and reporting guidance when including these trials. The aim of this study was to assess the methodological and reporting practice of Cochrane reviews that included C-RCTs against criteria developed from existing guidance.Criteria were developed, based on methodological literature and personal experience supervising review production and quality. Criteria were grouped into four themes: identifying, reporting, assessing risk of bias, and analysing C-RCTs. The Cochrane Database of Systematic Reviews was searched (2nd December 2013, and the 50 most recent reviews that included C-RCTs were retrieved. Each review was then assessed using the criteria.The 50 reviews we identified were published by 26 Cochrane Review Groups between June 2013 and November 2013. For identifying C-RCTs, only 56% identified that C-RCTs were eligible for inclusion in the review in the eligibility criteria. For reporting C-RCTs, only eight (24% of the 33 reviews reported the method of cluster adjustment for their included C-RCTs. For assessing risk of bias, only one review assessed all five C-RCT-specific risk-of-bias criteria. For analysing C-RCTs, of the 27 reviews that presented unadjusted data, only nine (33% provided a warning that confidence intervals may be artificially narrow. Of the 34 reviews that reported data from unadjusted C-RCTs, only 13 (38% excluded the unadjusted results from the meta-analyses.The methodological and reporting practices in Cochrane reviews incorporating C-RCTs could be greatly improved, particularly with regard to analyses. Criteria developed as part of the current study could be used by review authors or editors to identify errors and improve the quality of published systematic reviews incorporating

  18. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2015-01-01

    Full Text Available Unsupervised data classification (or clustering analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

  19. Prevalence of cluster headache in the Republic of Georgia: results of a population-based study and methodological considerations

    DEFF Research Database (Denmark)

    Katsarava, Z; Dzagnidze, A; Kukava, M

    2009-01-01

    We present a study of the general-population prevalence of cluster headache in the Republic of Georgia and discuss the advantages and challenges of different methodological approaches. In a community-based survey, specially trained medical residents visited 500 adjacent households in the capital...... with possible cluster headache, who were then personally interviewed by one of two headache-experienced neurologists. Cluster headache was confirmed in one subject. The prevalence of cluster headache was therefore estimated to be 87/100,000 (95% confidence interval

  20. Integrating the Supervised Information into Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    Ping Ling

    2013-01-01

    Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

  1. How effective is the comprehensive approach to rehabilitation (CARe) methodology? A cluster randomized controlled trial.

    Science.gov (United States)

    Bitter, Neis; Roeg, Diana; van Assen, Marcel; van Nieuwenhuizen, Chijs; van Weeghel, Jaap

    2017-12-11

    The CARe methodology aims to improve the quality of life of people with severe mental illness by supporting them in realizing their goals, handling their vulnerability and improving the quality of their social environment. This study aims to investigate the effectiveness of the CARe methodology for people with severe mental illness on their quality of life, personal recovery, participation, hope, empowerment, self-efficacy beliefs and unmet needs. A cluster Randomized Controlled Trial (RCT) was conducted in 14 teams of three organizations for sheltered and supported housing in the Netherlands. Teams in the intervention group received training in the CARe methodology. Teams in the control group continued working according to care as usual. Questionnaires were filled out at baseline, after 10 months and after 20 months. A total of 263 clients participated in the study. Quality of life increased in both groups, however, no differences between the intervention and control group were found. Recovery and social functioning did not change over time. Regarding the secondary outcomes, the number of unmet needs decreased in both groups. All intervention teams received the complete training program. The model fidelity at T1 was 53.4% for the intervention group and 33.4% for the control group. At T2 this was 50.6% for the intervention group and 37.2% for the control group. All clients improved in quality of life. However we did not find significant differences between the clients of the both conditions on any outcome measure. Possible explanations of these results are: the difficulty to implement rehabilitation-supporting practice, the content of the methodology and the difficulty to improve the lives of a group of people with longstanding and severe impairments in a relatively short period. More research is needed on how to improve effects of rehabilitation trainings in practice and on outcome level. ISRCTN77355880 , retrospectively registered (05/07/2013).

  2. Data clustering theory, algorithms, and applications

    CERN Document Server

    Gan, Guojun; Wu, Jianhong

    2007-01-01

    Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB® programming languages.

  3. Semi-supervised and unsupervised extreme learning machines.

    Science.gov (United States)

    Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

    2014-12-01

    Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

  4. Dimensionality reduction with unsupervised nearest neighbors

    CERN Document Server

    Kramer, Oliver

    2013-01-01

    This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...

  5. Unsupervised Document Embedding With CNNs

    OpenAIRE

    Liu, Chundi; Zhao, Shunan; Volkovs, Maksims

    2017-01-01

    We propose a new model for unsupervised document embedding. Leading existing approaches either require complex inference or use recurrent neural networks (RNN) that are difficult to parallelize. We take a different route and develop a convolutional neural network (CNN) embedding model. Our CNN architecture is fully parallelizable resulting in over 10x speedup in inference time over RNN models. Parallelizable architecture enables to train deeper models where each successive layer has increasin...

  6. Towards a methodology for cluster searching to provide conceptual and contextual "richness" for systematic reviews of complex interventions: case study (CLUSTER).

    Science.gov (United States)

    Booth, Andrew; Harris, Janet; Croot, Elizabeth; Springett, Jane; Campbell, Fiona; Wilkins, Emma

    2013-09-28

    Systematic review methodologies can be harnessed to help researchers to understand and explain how complex interventions may work. Typically, when reviewing complex interventions, a review team will seek to understand the theories that underpin an intervention and the specific context for that intervention. A single published report from a research project does not typically contain this required level of detail. A review team may find it more useful to examine a "study cluster"; a group of related papers that explore and explain various features of a single project and thus supply necessary detail relating to theory and/or context.We sought to conduct a preliminary investigation, from a single case study review, of techniques required to identify a cluster of related research reports, to document the yield from such methods, and to outline a systematic methodology for cluster searching. In a systematic review of community engagement we identified a relevant project - the Gay Men's Task Force. From a single "key pearl citation" we conducted a series of related searches to find contextually or theoretically proximate documents. We followed up Citations, traced Lead authors, identified Unpublished materials, searched Google Scholar, tracked Theories, undertook ancestry searching for Early examples and followed up Related projects (embodied in the CLUSTER mnemonic). Our structured, formalised procedure for cluster searching identified useful reports that are not typically identified from topic-based searches on bibliographic databases. Items previously rejected by an initial sift were subsequently found to inform our understanding of underpinning theory (for example Diffusion of Innovations Theory), context or both. Relevant material included book chapters, a Web-based process evaluation, and peer reviewed reports of projects sharing a common ancestry. We used these reports to understand the context for the intervention and to explore explanations for its relative

  7. Segmentation of fluorescence microscopy cell images using unsupervised mining.

    Science.gov (United States)

    Du, Xian; Dua, Sumeet

    2010-05-28

    The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

  8. An Unsupervised Online Spike-Sorting Framework.

    Science.gov (United States)

    Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

    2016-08-01

    Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

  9. Unsupervised spike sorting based on discriminative subspace learning.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

  10. A novel unsupervised spike sorting algorithm for intracranial EEG.

    Science.gov (United States)

    Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

    2011-01-01

    This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

  11. Unsupervised classification of major depression using functional connectivity MRI.

    Science.gov (United States)

    Zeng, Ling-Li; Shen, Hui; Liu, Li; Hu, Dewen

    2014-04-01

    The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional connectivity patterns and showed that a maximum margin clustering-based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group-level clustering consistency of 92.5% and an individual-level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering-based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Copyright © 2013 Wiley Periodicals, Inc.

  12. The Hubble Space Telescope Medium Deep Survey Cluster Sample: Methodology and Data

    Science.gov (United States)

    Ostrander, E. J.; Nichol, R. C.; Ratnatunga, K. U.; Griffiths, R. E.

    1998-12-01

    We present a new, objectively selected, sample of galaxy overdensities detected in the Hubble Space Telescope Medium Deep Survey (MDS). These clusters/groups were found using an automated procedure that involved searching for statistically significant galaxy overdensities. The contrast of the clusters against the field galaxy population is increased when morphological data are used to search around bulge-dominated galaxies. In total, we present 92 overdensities above a probability threshold of 99.5%. We show, via extensive Monte Carlo simulations, that at least 60% of these overdensities are likely to be real clusters and groups and not random line-of-sight superpositions of galaxies. For each overdensity in the MDS cluster sample, we provide a richness and the average of the bulge-to-total ratio of galaxies within each system. This MDS cluster sample potentially contains some of the most distant clusters/groups ever detected, with about 25% of the overdensities having estimated redshifts z > ~0.9. We have made this sample publicly available to facilitate spectroscopic confirmation of these clusters and help more detailed studies of cluster and galaxy evolution. We also report the serendipitous discovery of a new cluster close on the sky to the rich optical cluster Cl l0016+16 at z = 0.546. This new overdensity, HST 001831+16208, may be coincident with both an X-ray source and a radio source. HST 001831+16208 is the third cluster/group discovered near to Cl 0016+16 and appears to strengthen the claims of Connolly et al. of superclustering at high redshift.

  13. Information and methodological support of the cluster of children wellness recreation

    Directory of Open Access Journals (Sweden)

    Ivanova Svetlana

    2016-02-01

    Full Text Available The article considers the instrument of transfer of pedagogical experience of the staff at children's health camps, which can be used as one of the components of the cluster to ensure the functioning of the health-educational system children's health camps. The transformation of the notion "cluster" from economic category in social and pedagogical is shown

  14. Unsupervised image matching based on manifold alignment.

    Science.gov (United States)

    Pei, Yuru; Huang, Fengchun; Shi, Fuhao; Zha, Hongbin

    2012-08-01

    This paper challenges the issue of automatic matching between two image sets with similar intrinsic structures and different appearances, especially when there is no prior correspondence. An unsupervised manifold alignment framework is proposed to establish correspondence between data sets by a mapping function in the mutual embedding space. We introduce a local similarity metric based on parameterized distance curves to represent the connection of one point with the rest of the manifold. A small set of valid feature pairs can be found without manual interactions by matching the distance curve of one manifold with the curve cluster of the other manifold. To avoid potential confusions in image matching, we propose an extended affine transformation to solve the nonrigid alignment in the embedding space. The comparatively tight alignments and the structure preservation can be obtained simultaneously. The point pairs with the minimum distance after alignment are viewed as the matchings. We apply manifold alignment to image set matching problems. The correspondence between image sets of different poses, illuminations, and identities can be established effectively by our approach.

  15. Unsupervised Learning of Action Primitives

    DEFF Research Database (Denmark)

    Baby, Sanmohan; Krüger, Volker; Kragic, Danica

    2010-01-01

    and scale, the use of the object can provide a strong invariant for the detection of motion primitives. In this paper we propose an unsupervised learning approach for action primitives that makes use of the human movements as well as the object state changes. We group actions according to the changes......Action representation is a key issue in imitation learning for humanoids. With the recent finding of mirror neurons there has been a growing interest in expressing actions as a combination meaningful subparts called primitives. Primitives could be thought of as an alphabet for the human actions....... In this paper we observe that human actions and objects can be seen as being intertwined: we can interpret actions from the way the body parts are moving, but as well from how their effect on the involved object. While human movements can look vastly different even under minor changes in location, orientation...

  16. A Clustering Methodology of Web Log Data for Learning Management Systems

    Science.gov (United States)

    Valsamidis, Stavros; Kontogiannis, Sotirios; Kazanidis, Ioannis; Theodosiou, Theodosios; Karakos, Alexandros

    2012-01-01

    Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov…

  17. Bayesian feature weighting for unsupervised learning, with application to object recognition

    OpenAIRE

    Carbonetto , Peter; De Freitas , Nando; Gustafson , Paul; Thompson , Natalie

    2003-01-01

    International audience; We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.

  18. Fuzzy Clustering based Methodology for Multidimensional Data Analysis in Computational Forensic Domain

    OpenAIRE

    Kilian Stoffel; Paul Cotofrei; Dong Han

    2012-01-01

    As interdisciplinary domain requiring advanced and innovative methodologies the computational forensics domain is characterized by data being simultaneously large scaled and uncertain multidimensional and approximate. Forensic domain experts trained to discover hidden pattern from crime data are limited in their analysis without the assistance of a computational intelligence approach. In this paper a methodology and an automatic procedure based on fuzzy set theory and designed to infer precis...

  19. Modeling electronic defects in La2CuO4 and LiCl using embedded quantum cluster methodology

    International Nuclear Information System (INIS)

    Grimes, R.W.; Shluger, A.L.; Baetzold, R.; Catlow, C.R.A.

    1991-01-01

    By exploiting recent developments in computer simulation methods the authors modeled the behavior of hole states in La 2 CuO 4 and excited state defects such as the exciton in LiCl. The authors methodology employs a Hartree-Fock cluster embedded in a classical surround. Although the method is discussed with respect to the hole and exciton defects in particular, the scope of the talk includes other material problems currently being investigated by this method. Thus, the types of problems for which the method are appropriate are illustrated and the present limitations are discussed

  20. A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

    Science.gov (United States)

    Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

    2015-10-01

    Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

  1. Methodology сomparative statistical analysis of Russian industry based on cluster analysis

    Directory of Open Access Journals (Sweden)

    Sergey S. Shishulin

    2017-01-01

    Full Text Available The article is devoted to researching of the possibilities of applying multidimensional statistical analysis in the study of industrial production on the basis of comparing its growth rates and structure with other developed and developing countries of the world. The purpose of this article is to determine the optimal set of statistical methods and the results of their application to industrial production data, which would give the best access to the analysis of the result.Data includes such indicators as output, output, gross value added, the number of employed and other indicators of the system of national accounts and operational business statistics. The objects of observation are the industry of the countrys of the Customs Union, the United States, Japan and Erope in 2005-2015. As the research tool used as the simplest methods of transformation, graphical and tabular visualization of data, and methods of statistical analysis. In particular, based on a specialized software package (SPSS, the main components method, discriminant analysis, hierarchical methods of cluster analysis, Ward’s method and k-means were applied.The application of the method of principal components to the initial data makes it possible to substantially and effectively reduce the initial space of industrial production data. Thus, for example, in analyzing the structure of industrial production, the reduction was from fifteen industries to three basic, well-interpreted factors: the relatively extractive industries (with a low degree of processing, high-tech industries and consumer goods (medium-technology sectors. At the same time, as a result of comparison of the results of application of cluster analysis to the initial data and data obtained on the basis of the principal components method, it was established that clustering industrial production data on the basis of new factors significantly improves the results of clustering.As a result of analyzing the parameters of

  2. An unsupervised text mining method for relation extraction from biomedical literature.

    Directory of Open Access Journals (Sweden)

    Changqin Quan

    Full Text Available The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1 Protein-protein interactions extraction, and (2 Gene-suicide association extraction. The evaluation of task (1 on the benchmark dataset (AImed corpus showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

  3. Unsupervised daily routine and activity discovery in smart homes.

    Science.gov (United States)

    Jie Yin; Qing Zhang; Karunanithi, Mohan

    2015-08-01

    The ability to accurately recognize daily activities of residents is a core premise of smart homes to assist with remote health monitoring. Most of the existing methods rely on a supervised model trained from a preselected and manually labeled set of activities, which are often time-consuming and costly to obtain in practice. In contrast, this paper presents an unsupervised method for discovering daily routines and activities for smart home residents. Our proposed method first uses a Markov chain to model a resident's locomotion patterns at different times of day and discover clusters of daily routines at the macro level. For each routine cluster, it then drills down to further discover room-level activities at the micro level. The automatic identification of daily routines and activities is useful for understanding indicators of functional decline of elderly people and suggesting timely interventions.

  4. The Development Of Red Chili Agribusiness Cluster With Soft System Methodology (Ssm Approach In Garut, West Java

    Directory of Open Access Journals (Sweden)

    Sri Ayu Andayani

    2016-12-01

    Full Text Available Red chili is one of the commodities with high price fluctuation and gives influence to inflation. It happens due to the unsustainable supply of red chili from the central production centers to the market. Bank Indonesia (central bank initiates a cluster system to support price controlling and regional economic growth. In this regard, the study is conducted  in Garut regency, which is one of the centers of red chili plantation in West Java and uses as cluster development, and yet there are still many obstacles along the way. This paper has the objective to describe the problem which causes unsustainable production and affects industrial supplies systemically and also to analyze the existing partnerships in order to maintain the continuity of supply as an alternative solution.This study was designed qualitatively with case study method through a system approach namely soft system methodology (SSM. The results shows that the problems in the cluster of red chili are ranging from production planning to the delay of sales payment process which systemically interlinked and the collaboration of executors that have not been optimally implemented. This study offers solution for those problems accordance with change formulation of SSM and industrial emphasis on fairness, transparency and integrated optimization with the principle of production sustainability from all stakeholders through participative collaboration to maintain continuity of production.

  5. An alternative methodological approach to value analysis of regions, municipal corporations and clusters

    Directory of Open Access Journals (Sweden)

    Mojmír Sabolovič

    2011-01-01

    Full Text Available The paper deals with theoretical conception of value analysis of regions, municipal corporations and clusters. The subject of this paper is heterodox approach to sensitivity analysis of finite set of variables based on non-additive measure. For dynamic analysis of trajectory of general value are sufficient robust models based on maximum entropy principle. Findings concern explanation of proper fuzzy integral – Choquet integral. The fuzzy measure is represented by theory of capacities (Choquet, 1953 on powerset. In fine, the conception of the New integral for capacities (Lehler, 2005 is discussed. Value analysis and transmission constitutes remarkable aspect of performance evaluation of regions, municipal corporations and clusters. In the light of high ratio of soft variables, social behavior, intangible assets and human capital within those types of subjects the fuzzy integral introduce useful tool for modeling. The New integral afterwards concerns considerable characteristic of people behavior – risk averse articulated concave function and non-additive operator. Results comprehended tools enabling observation of synergy, redundancy and inhibition of value variables as consequence of non-additive measure. In fine, results induced issues for future research.

  6. clusters

    Indian Academy of Sciences (India)

    2017-09-27

    Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

  7. clusters

    Indian Academy of Sciences (India)

    environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.

  8. Unsupervised Performance Evaluation of Image Segmentation

    Directory of Open Access Journals (Sweden)

    Chabrier Sebastien

    2006-01-01

    Full Text Available We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

  9. Partitional clustering algorithms

    CERN Document Server

    2015-01-01

    This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

  10. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

    Science.gov (United States)

    Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

    2017-10-03

    One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to

  11. Unsupervised Classification of Surface Defects in Wire Rod Production Obtained by Eddy Current Sensors

    Directory of Open Access Journals (Sweden)

    Sergio Saludes-Rodil

    2015-04-01

    Full Text Available An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors.

  12. Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2012-07-01

    Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.

  13. A competition in unsupervised color image segmentation

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Mikeš, Stanislav

    2016-01-01

    Roč. 57, č. 9 (2016), s. 136-151 ISSN 0031-3203 R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : Unsupervised image segmentation * Segmentation contest * Texture analysis Subject RIV: BD - Theory of Information Impact factor: 4.582, year: 2016 http://library.utia.cas.cz/separaty/2016/RO/haindl-0459179.pdf

  14. Unsupervised classification of multivariate geostatistical data: Two algorithms

    Science.gov (United States)

    Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

    2015-12-01

    With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

  15. An unsupervised strategy for biomedical image segmentation

    Directory of Open Access Journals (Sweden)

    Roberto Rodríguez

    2010-09-01

    Full Text Available Roberto Rodríguez1, Rubén Hernández21Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, MexicoAbstract: Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.Keywords: segmentation, mean shift, unsupervised segmentation, entropy

  16. Unsupervised text mining for assessing and augmenting GWAS results.

    Science.gov (United States)

    Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

    2016-04-01

    Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Unsupervised EEG analysis for automated epileptic seizure detection

    Science.gov (United States)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  18. Automatic microseismic event picking via unsupervised machine learning

    Science.gov (United States)

    Chen, Yangkang

    2018-01-01

    Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

  19. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...

    Data.gov (United States)

    National Aeronautics and Space Administration — Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Abstract: This article describes the results of applying four...

  20. Unsupervised Power Profiling for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Blunck, Henrik

    Today, power consumption is a main limitation for mobile phones. To minimize the power consumption of popular and traditionally power-hungry location-based services requires knowledge of how individual phone features consume power, so that those features can be utilized intelligently for optimal...... power savings while at the same time maintaining good quality of service. This paper proposes an unsupervised API-level method for power profiling mobile phones based on genetic algorithms. The method enables accurate profiling of the power consumption of devices and thereby provides the information...

  1. Unsupervised Power Profiling for Mobile Devices

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Blunck, Henrik

    2011-01-01

    Today, power consumption is a main limitation for mobile phones. To minimize the power consumption of popular and traditionally power-hungry location-based services requires knowledge of how individual phone features consume power, so that those features can be utilized intelligently for optimal...... power savings while at the same time maintaining good quality of service. This paper proposes an unsupervised API-level method for power profiling mobile phones based on genetic algorithms. The method enables accurate profiling of the power consumption of devices and thereby provides the information...

  2. Unsupervised information extraction by text segmentation

    CERN Document Server

    Cortez, Eli

    2013-01-01

    A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors' approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a

  3. Horticultural cluster

    OpenAIRE

    SHERSTIUK S.V.; POSYLAYEVA K.I.

    2013-01-01

    In the article there are the theoretical and methodological approaches to the nature and existence of the cluster. The cluster differences from other kinds of cooperative and integration associations. Was develop by scientific-practical recommendations for forming a competitive horticultur cluster.

  4. Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

    Science.gov (United States)

    Steinley, Douglas; Brusco, Michael J.

    2011-01-01

    This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

  5. Unsupervised learning of facial emotion decoding skills

    Directory of Open Access Journals (Sweden)

    Jan Oliver Huelle

    2014-02-01

    Full Text Available Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practise without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear and sadness was shown in each clip. Although no external information about the correctness of the participant’s response or the sender’s true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practise effects often observed in cognitive tasks.

  6. Unsupervised learning of facial emotion decoding skills.

    Science.gov (United States)

    Huelle, Jan O; Sack, Benjamin; Broer, Katja; Komlewa, Irina; Anders, Silke

    2014-01-01

    Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practice without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear, and sadness) was shown in each clip. Although no external information about the correctness of the participant's response or the sender's true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practice effects often observed in cognitive tasks.

  7. Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

    Directory of Open Access Journals (Sweden)

    Marco Borri

    Full Text Available To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment.The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4. Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters.The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4, determined with cluster validation, produced the best separation between reducing and non-reducing clusters.The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

  8. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  9. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  10. Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

    Science.gov (United States)

    Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

    2018-01-01

    Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a

  11. Unsupervised action classification using space-time link analysis

    DEFF Research Database (Denmark)

    Liu, Haowei; Feris, Rogerio; Krüger, Volker

    2010-01-01

    In this paper we address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which matches the performance of traditional unsupervised action categorization metho...

  12. Emulating galaxy clustering and galaxy-galaxy lensing into the deeply nonlinear regime: methodology, information, and forecasts

    OpenAIRE

    Wibking, Benjamin D.; Salcedo, Andrés N.; Weinberg, David H.; Garrison, Lehman H.; Ferrer, Douglas; Tinker, Jeremy; Eisenstein, Daniel; Metchnik, Marc; Pinto, Philip

    2017-01-01

    The combination of galaxy-galaxy lensing (GGL) with galaxy clustering is one of the most promising routes to determining the amplitude of matter clustering at low redshifts. We show that extending clustering+GGL analyses from the linear regime down to $\\sim 0.5 \\, h^{-1}$ Mpc scales increases their constraining power considerably, even after marginalizing over a flexible model of non-linear galaxy bias. Using a grid of cosmological N-body simulations, we construct a Taylor-expansion emulator ...

  13. A new hybrid imperialist competitive algorithm on data clustering

    Indian Academy of Sciences (India)

    Modified imperialist competitive algorithm; simulated annealing; ... Clustering is one of the unsupervised learning branches where a set of patterns, usually vectors ..... machine classification is based on design, operation, and/or purpose.

  14. Unsupervised learning of a steerable basis for invariant image representations

    Science.gov (United States)

    Bethge, Matthias; Gerwinn, Sebastian; Macke, Jakob H.

    2007-02-01

    There are two aspects to unsupervised learning of invariant representations of images: First, we can reduce the dimensionality of the representation by finding an optimal trade-off between temporal stability and informativeness. We show that the answer to this optimization problem is generally not unique so that there is still considerable freedom in choosing a suitable basis. Which of the many optimal representations should be selected? Here, we focus on this second aspect, and seek to find representations that are invariant under geometrical transformations occuring in sequences of natural images. We utilize ideas of 'steerability' and Lie groups, which have been developed in the context of filter design. In particular, we show how an anti-symmetric version of canonical correlation analysis can be used to learn a full-rank image basis which is steerable with respect to rotations. We provide a geometric interpretation of this algorithm by showing that it finds the two-dimensional eigensubspaces of the average bivector. For data which exhibits a variety of transformations, we develop a bivector clustering algorithm, which we use to learn a basis of generalized quadrature pairs (i.e. 'complex cells') from sequences of natural images.

  15. Unsupervised Tensor Mining for Big Data Practitioners.

    Science.gov (United States)

    Papalexakis, Evangelos E; Faloutsos, Christos

    2016-09-01

    Multiaspect data are ubiquitous in modern Big Data applications. For instance, different aspects of a social network are the different types of communication between people, the time stamp of each interaction, and the location associated to each individual. How can we jointly model all those aspects and leverage the additional information that they introduce to our analysis? Tensors, which are multidimensional extensions of matrices, are a principled and mathematically sound way of modeling such multiaspect data. In this article, our goal is to popularize tensors and tensor decompositions to Big Data practitioners by demonstrating their effectiveness, outlining challenges that pertain to their application in Big Data scenarios, and presenting our recent work that tackles those challenges. We view this work as a step toward a fully automated, unsupervised tensor mining tool that can be easily and broadly adopted by practitioners in academia and industry.

  16. Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

    Science.gov (United States)

    Doran, G.

    2013-01-01

    In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

  17. Unsupervised text mining methods for literature analysis: a case study for Thomas Pynchon's V.

    Directory of Open Access Journals (Sweden)

    Christos Iraklis Tsatsoulis

    2013-08-01

    Full Text Available We investigate the use of unsupervised text mining methods for the analysis of prose literature works, using Thomas Pynchon's novel 'V'. as a case study. Our results suggest that such methods may be employed to reveal meaningful information regarding the novel’s structure. We report results using a wide variety of clustering algorithms, several distinct distance functions, and different visualization techniques. The application of a simple topic model is also demonstrated. We discuss the meaningfulness of our results along with the limitations of our approach, and we suggest some possible paths for further study.

  18. Rational Variety Mapping for Contrast-Enhanced Nonlinear Unsupervised Segmentation of Multispectral Images of Unstained Specimen

    Science.gov (United States)

    Kopriva, Ivica; Hadžija, Mirko; Popović Hadžija, Marijana; Korolija, Marina; Cichocki, Andrzej

    2011-01-01

    A methodology is proposed for nonlinear contrast-enhanced unsupervised segmentation of multispectral (color) microscopy images of principally unstained specimens. The methodology exploits spectral diversity and spatial sparseness to find anatomical differences between materials (cells, nuclei, and background) present in the image. It consists of rth-order rational variety mapping (RVM) followed by matrix/tensor factorization. Sparseness constraint implies duality between nonlinear unsupervised segmentation and multiclass pattern assignment problems. Classes not linearly separable in the original input space become separable with high probability in the higher-dimensional mapped space. Hence, RVM mapping has two advantages: it takes implicitly into account nonlinearities present in the image (ie, they are not required to be known) and it increases spectral diversity (ie, contrast) between materials, due to increased dimensionality of the mapped space. This is expected to improve performance of systems for automated classification and analysis of microscopic histopathological images. The methodology was validated using RVM of the second and third orders of the experimental multispectral microscopy images of unstained sciatic nerve fibers (nervus ischiadicus) and of unstained white pulp in the spleen tissue, compared with a manually defined ground truth labeled by two trained pathophysiologists. The methodology can also be useful for additional contrast enhancement of images of stained specimens. PMID:21708116

  19. Supervised and Unsupervised Classification for Pattern Recognition Purposes

    Directory of Open Access Journals (Sweden)

    Catalina COCIANU

    2006-01-01

    Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

  20. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

    Science.gov (United States)

    Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

    2012-09-01

    Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Cluster-cluster clustering

    International Nuclear Information System (INIS)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

  2. Klastery v institucional'noj proekcii: k teorii i metodologii lokal'nogo social'no-jekonomicheskogo razvitija [Clusters in the institutional perspective: on the theory and methodology of local socioeconomic development

    Directory of Open Access Journals (Sweden)

    Gareev Timur

    2012-01-01

    Full Text Available This article addresses the problem of definition and identification of clusters as localized mesoeconomic systems with fuzzy boundaries that stimulate the development of these systems. The author analyses the influence of the inductive approach to the formation of cluster theory and juxtaposes different typologies of clusters and other types of localized economic systems. The article offers an overview of the existing methodological approaches to the problem of cluster identification and emphasises the major role of institutional dimension in the identification (and functioning of clusters, especially in comparison to cluster formation theory based on the technological connection of adjacent units. The author comes to a conclusion that, without the inclusion of institutional factors, alongside localising and technological ones (demonstrated through different variables, it is virtually impossible to develop an independent cluster theory, different from the general agglomeration theory. For the first time, a hierarchy of institutions affecting the formation of local economic systems is considered against the background of the identification of institutional levels, whose full development makes it possible to speak of the formation of clusters as most successful mesoeconomic systems. At the same time, the author emphasizes that, in economies gravitating towards the market type of organisation, the development of mesoeconomic systems is closely connected to competition for innovative rent. The article outlines the methodology for cluster studies, which makes it possible to consider such relatively new to the regional science phenomena as innovative and “transborder” clusters.

  3. Concept formation knowledge and experience in unsupervised learning

    CERN Document Server

    Fisher, Douglas H; Langley, Pat

    1991-01-01

    Concept Formation: Knowledge and Experience in Unsupervised Learning presents the interdisciplinary interaction between machine learning and cognitive psychology on unsupervised incremental methods. This book focuses on measures of similarity, strategies for robust incremental learning, and the psychological consistency of various approaches.Organized into three parts encompassing 15 chapters, this book begins with an overview of inductive concept learning in machine learning and psychology, with emphasis on issues that distinguish concept formation from more prevalent supervised methods and f

  4. Unsupervised grammar induction of clinical report sublanguage.

    Science.gov (United States)

    Kate, Rohit J

    2012-10-05

    Clinical reports are written using a subset of natural language while employing many domain-specific terms; such a language is also known as a sublanguage for a scientific or a technical domain. Different genres of clinical reports use different sublaguages, and in addition, different medical facilities use different medical language conventions. This makes supervised training of a parser for clinical sentences very difficult as it would require expensive annotation effort to adapt to every type of clinical text. In this paper, we present an unsupervised method which automatically induces a grammar and a parser for the sublanguage of a given genre of clinical reports from a corpus with no annotations. In order to capture sentence structures specific to clinical domains, the grammar is induced in terms of semantic classes of clinical terms in addition to part-of-speech tags. Our method induces grammar by minimizing the combined encoding cost of the grammar and the corresponding sentence derivations. The probabilities for the productions of the induced grammar are then learned from the unannotated corpus using an instance of the expectation-maximization algorithm. Our experiments show that the induced grammar is able to parse novel sentences. Using a dataset of discharge summary sentences with no annotations, our method obtains 60.5% F-measure for parse-bracketing on sentences of maximum length 10. By varying a parameter, the method can induce a range of grammars, from very specific to very general, and obtains the best performance in between the two extremes.

  5. Unsupervised Retinal Vessel Segmentation Using Combined Filters.

    Directory of Open Access Journals (Sweden)

    Wendeson S Oliveira

    Full Text Available Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

  6. ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

    Directory of Open Access Journals (Sweden)

    Halfdan Rydbeck

    Full Text Available Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

  7. Joint analysis of galaxy-galaxy lensing and galaxy clustering: Methodology and forecasts for Dark Energy Survey

    International Nuclear Information System (INIS)

    Park, Y.; Krause, E.; Dodelson, S.; Jain, B.; Amara, A.

    2016-01-01

    Here, the joint analysis of galaxy-galaxy lensing and galaxy clustering is a promising method for inferring the growth function of large-scale structure. Anticipating a near future application of this analysis to Dark Energy Survey (DES) measurements of galaxy positions and shapes, we develop a practical approach to modeling the assumptions and systematic effects affecting the joint analysis of small-scale galaxy-galaxy lensing and large-scale galaxy clustering. Introducing parameters that characterize the halo occupation distribution (HOD), photometric redshift uncertainties, and shear measurement errors, we study how external priors on different subsets of these parameters affect our growth constraints. Degeneracies within the HOD model, as well as between the HOD and the growth function, are identified as the dominant source of complication, with other systematic effects being subdominant. The impact of HOD parameters and their degeneracies necessitate the detailed joint modeling of the galaxy sample that we employ. We conclude that DES data will provide powerful constraints on the evolution of structure growth in the Universe, conservatively/optimistically constraining the growth function to 7.9%/4.8% with its first-year data that cover over 1000 square degrees, and to 3.9%/2.3% with its full five-year data that will survey 5000 square degrees, including both statistical and systematic uncertainties.

  8. Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

    Science.gov (United States)

    Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

    2018-01-11

    By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

  9. Unsupervised grammar induction of clinical report sublanguage

    Directory of Open Access Journals (Sweden)

    Kate Rohit J

    2012-10-01

    Full Text Available Abstract Background Clinical reports are written using a subset of natural language while employing many domain-specific terms; such a language is also known as a sublanguage for a scientific or a technical domain. Different genres of clinical reports use different sublaguages, and in addition, different medical facilities use different medical language conventions. This makes supervised training of a parser for clinical sentences very difficult as it would require expensive annotation effort to adapt to every type of clinical text. Methods In this paper, we present an unsupervised method which automatically induces a grammar and a parser for the sublanguage of a given genre of clinical reports from a corpus with no annotations. In order to capture sentence structures specific to clinical domains, the grammar is induced in terms of semantic classes of clinical terms in addition to part-of-speech tags. Our method induces grammar by minimizing the combined encoding cost of the grammar and the corresponding sentence derivations. The probabilities for the productions of the induced grammar are then learned from the unannotated corpus using an instance of the expectation-maximization algorithm. Results Our experiments show that the induced grammar is able to parse novel sentences. Using a dataset of discharge summary sentences with no annotations, our method obtains 60.5% F-measure for parse-bracketing on sentences of maximum length 10. By varying a parameter, the method can induce a range of grammars, from very specific to very general, and obtains the best performance in between the two extremes.

  10. Estimating extinction using unsupervised machine learning

    Science.gov (United States)

    Meingast, Stefan; Lombardi, Marco; Alves, João

    2017-05-01

    Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, Pnicer, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction toward single sources is determined by fitting Gaussian mixture models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities, rather than a single value. Pnicer effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, Pnicer offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. The Pnicer software package also offers access to the well-established Nicer technique in a simple unified interface and is capable of building extinction maps including the Nicest correction for cloud substructure. Pnicer is offered to the community as an open-source software solution and is entirely written in Python.

  11. UNSUPERVISED TRANSIENT LIGHT CURVE ANALYSIS VIA HIERARCHICAL BAYESIAN INFERENCE

    Energy Technology Data Exchange (ETDEWEB)

    Sanders, N. E.; Soderberg, A. M. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Betancourt, M., E-mail: nsanders@cfa.harvard.edu [Department of Statistics, University of Warwick, Coventry CV4 7AL (United Kingdom)

    2015-02-10

    Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nascent era of wide field transient searches, objects with detailed observations are decreasing as a fraction of the overall known SN population, and this strategy sacrifices the majority of the information contained in the data about the underlying population of transients. A population level modeling approach, simultaneously fitting all available observations of objects in a transient sub-class of interest, fully mines the data to infer the properties of the population and avoids certain systematic biases. We present a novel hierarchical Bayesian statistical model for population level modeling of transient light curves, and discuss its implementation using an efficient Hamiltonian Monte Carlo technique. As a test case, we apply this model to the Type IIP SN sample from the Pan-STARRS1 Medium Deep Survey, consisting of 18,837 photometric observations of 76 SNe, corresponding to a joint posterior distribution with 9176 parameters under our model. Our hierarchical model fits provide improved constraints on light curve parameters relevant to the physical properties of their progenitor stars relative to modeling individual light curves alone. Moreover, we directly evaluate the probability for occurrence rates of unseen light curve characteristics from the model hyperparameters, addressing observational biases in survey methodology. We view this modeling framework as an unsupervised machine learning technique with the ability to maximize scientific returns from data to be collected by future wide field transient searches like LSST.

  12. UNSUPERVISED TRANSIENT LIGHT CURVE ANALYSIS VIA HIERARCHICAL BAYESIAN INFERENCE

    International Nuclear Information System (INIS)

    Sanders, N. E.; Soderberg, A. M.; Betancourt, M.

    2015-01-01

    Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nascent era of wide field transient searches, objects with detailed observations are decreasing as a fraction of the overall known SN population, and this strategy sacrifices the majority of the information contained in the data about the underlying population of transients. A population level modeling approach, simultaneously fitting all available observations of objects in a transient sub-class of interest, fully mines the data to infer the properties of the population and avoids certain systematic biases. We present a novel hierarchical Bayesian statistical model for population level modeling of transient light curves, and discuss its implementation using an efficient Hamiltonian Monte Carlo technique. As a test case, we apply this model to the Type IIP SN sample from the Pan-STARRS1 Medium Deep Survey, consisting of 18,837 photometric observations of 76 SNe, corresponding to a joint posterior distribution with 9176 parameters under our model. Our hierarchical model fits provide improved constraints on light curve parameters relevant to the physical properties of their progenitor stars relative to modeling individual light curves alone. Moreover, we directly evaluate the probability for occurrence rates of unseen light curve characteristics from the model hyperparameters, addressing observational biases in survey methodology. We view this modeling framework as an unsupervised machine learning technique with the ability to maximize scientific returns from data to be collected by future wide field transient searches like LSST

  13. The composite sequential clustering technique for analysis of multispectral scanner data

    Science.gov (United States)

    Su, M. Y.

    1972-01-01

    The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

  14. Methodological issues in the design and evaluation of supported communication for aphasia training: a cluster-controlled feasibility study.

    Science.gov (United States)

    Horton, Simon; Clark, Allan; Barton, Garry; Lane, Kathleen; Pomeroy, Valerie M

    2016-04-18

    To assess the feasibility and acceptability of training stroke service staff to provide supported communication for people with moderate-severe aphasia in the acute phase; assess the suitability of outcome measures; collect data to inform sample size and Health Economic evaluation in a definitive trial. Phase II cluster-controlled, observer-blinded feasibility study. In-patient stroke rehabilitation units in the UK matched for bed numbers and staffing were assigned to control and intervention conditions. 70 stroke rehabilitation staff from all professional groups, excluding doctors, were recruited. 20 patients with moderate-severe aphasia were recruited. Supported communication for aphasia training, adapted to the stroke unit context versus usual care. Training was supplemented by a staff learning log, refresher sessions and provision of communication resources. Feasibility of recruitment and acceptability of the intervention and of measures required to assess outcomes and Health Economic evaluation in a definitive trial. Staff outcomes: Measure of Support in Conversation; patient outcomes: Stroke and Aphasia Quality of Life Scale; Communicative Access Measure for Stroke; Therapy Outcome Measures for aphasia; EQ-5D-3L was used to assess health outcomes. Feasibility of staff recruitment was demonstrated. Training in the intervention was carried out with 28 staff and was found to be acceptable in qualitative reports. 20 patients consented to take part, 6 withdrew. 18 underwent all measures at baseline; 16 at discharge; and 14 at 6-month follow-up. Of 175 patients screened 71% were deemed to be ineligible, either lacking capacity or too unwell to participate. Poor completion rates impacted on assessment of patient outcomes. We were able to collect sufficient data at baseline, discharge and follow-up for economic evaluation. The feasibility study informed components of the intervention and implementation in day-to-day practice. Modifications to the design are needed

  15. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

  16. Statistical mechanics of semi-supervised clustering in sparse graphs

    International Nuclear Information System (INIS)

    Ver Steeg, Greg; Galstyan, Aram; Allahverdyan, Armen E

    2011-01-01

    We theoretically study semi-supervised clustering in sparse graphs in the presence of pair-wise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pair-wise constraints on the clustering accuracy. Our results suggest that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears

  17. Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

    Science.gov (United States)

    Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan; Lee, Intae; Jang, Giljin; Yousefi, Siamak; Zangwill, Linda M; Medeiros, Felipe A; Girkin, Christopher A; Liebmann, Jeffrey M; Goldbaum, Michael H

    2014-01-01

    The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

  18. Dark Energy Survey Year 1 Results: Methodology and Projections for Joint Analysis of Galaxy Clustering, Galaxy Lensing, and CMB Lensing Two-point Functions

    Energy Technology Data Exchange (ETDEWEB)

    Giannantonio, T.; et al.

    2018-02-14

    Optical imaging surveys measure both the galaxy density and the gravitational lensing-induced shear fields across the sky. Recently, the Dark Energy Survey (DES) collaboration used a joint fit to two-point correlations between these observables to place tight constraints on cosmology (DES Collaboration et al. 2017). In this work, we develop the methodology to extend the DES Collaboration et al. (2017) analysis to include cross-correlations of the optical survey observables with gravitational lensing of the cosmic microwave background (CMB) as measured by the South Pole Telescope (SPT) and Planck. Using simulated analyses, we show how the resulting set of five two-point functions increases the robustness of the cosmological constraints to systematic errors in galaxy lensing shear calibration. Additionally, we show that contamination of the SPT+Planck CMB lensing map by the thermal Sunyaev-Zel'dovich effect is a potentially large source of systematic error for two-point function analyses, but show that it can be reduced to acceptable levels in our analysis by masking clusters of galaxies and imposing angular scale cuts on the two-point functions. The methodology developed here will be applied to the analysis of data from the DES, the SPT, and Planck in a companion work.

  19. Unsupervised Condition Change Detection In Large Diesel Engines

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan

    2003-01-01

    This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investig...... be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.......This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can...

  20. Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

    2013-01-01

    Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

  1. Deep unsupervised learning on a desktop PC: A primer for cognitive scientists

    Directory of Open Access Journals (Sweden)

    Alberto eTestolin

    2013-05-01

    Full Text Available Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programming parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low-cost graphic cards (GPUs without any specific programming effort, thanks to the use of high-level programming routines (available in MATLAB or Python. We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

  2. GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

    Science.gov (United States)

    Wagner, Florian

    2015-01-01

    Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

  3. Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

    2013-01-01

    Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

  4. Shadow detection and removal in RGB VHR images for land use unsupervised classification

    Science.gov (United States)

    Movia, A.; Beinat, A.; Crosilla, F.

    2016-09-01

    Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

  5. Automated age-related macular degeneration classification in OCT using unsupervised feature learning

    Science.gov (United States)

    Venhuizen, Freerk G.; van Ginneken, Bram; Bloemen, Bart; van Grinsven, Mark J. J. P.; Philipsen, Rick; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

    2015-03-01

    Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

  6. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  7. Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

    Science.gov (United States)

    Salman, S. S.; Abbas, W. A.

    2018-05-01

    The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

  8. K­MEANS CLUSTERING FOR HIDDEN MARKOV MODEL

    NARCIS (Netherlands)

    Perrone, M.P.; Connell, S.D.

    2004-01-01

    An unsupervised k­means clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as

  9. Single pass kernel k-means clustering method

    Indian Academy of Sciences (India)

    In unsupervised classification, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...

  10. Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

    Science.gov (United States)

    Rohrmeier, Martin A; Cross, Ian

    2014-07-01

    Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Specialization processes in on-line unsupervised learning

    NARCIS (Netherlands)

    Biehl, M.; Freking, A.; Reents, G.; Schlösser, E.

    1998-01-01

    From the recent analysis of supervised learning by on-line gradient descent in multilayered neural networks it is known that the necessary process of student specialization can be delayed significantly. We demonstrate that this phenomenon also occurs in various models of unsupervised learning. A

  12. Unsupervised Assessment of Subcutaneous and Visceral Fat by MRI

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Larsen, Rasmus; Wraae, Kristian

    2009-01-01

    This paper presents a. method for unsupervised assessment of visceral and subcutaneous adipose tissue in the abdominal region by MRI. The identification of the subcutaneous and the visceral regions were achieved by dynamic programming constrained by points acquired from an active shape model...

  13. Modeling Visit Behaviour in Smart Homes using Unsupervised Learning

    NARCIS (Netherlands)

    Nait Aicha, A.; Englebienne, G.; Kröse, B.

    2014-01-01

    Many algorithms on health monitoring from ambient sensor networks assume that only a single person is present in the home. We present an unsupervised method that models visit behaviour. A Markov modulated multidimensional non-homogeneous Poisson process (M3P2) is described that allows us to model

  14. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  15. Teacher and learner: Supervised and unsupervised learning in communities.

    Science.gov (United States)

    Shafto, Michael G; Seifert, Colleen M

    2015-01-01

    How far can teaching methods go to enhance learning? Optimal methods of teaching have been considered in research on supervised and unsupervised learning. Locally optimal methods are usually hybrids of teaching and self-directed approaches. The costs and benefits of specific methods have been shown to depend on the structure of the learning task, the learners, the teachers, and the environment.

  16. Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

    Science.gov (United States)

    Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

    2017-09-01

    This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert

  17. Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

    Science.gov (United States)

    Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

    2016-09-01

    Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

  18. Gastric cancer differentiation using Fourier transform near-infrared spectroscopy with unsupervised pattern recognition

    Science.gov (United States)

    Yi, Wei-song; Cui, Dian-sheng; Li, Zhi; Wu, Lan-lan; Shen, Ai-guo; Hu, Ji-ming

    2013-01-01

    The manuscript has investigated the application of near-infrared (NIR) spectroscopy for differentiation gastric cancer. The 90 spectra from cancerous and normal tissues were collected from a total of 30 surgical specimens using Fourier transform near-infrared spectroscopy (FT-NIR) equipped with a fiber-optic probe. Major spectral differences were observed in the CH-stretching second overtone (9000-7000 cm-1), CH-stretching first overtone (6000-5200 cm-1), and CH-stretching combination (4500-4000 cm-1) regions. By use of unsupervised pattern recognition, such as principal component analysis (PCA) and cluster analysis (CA), all spectra were classified into cancerous and normal tissue groups with accuracy up to 81.1%. The sensitivity and specificity was 100% and 68.2%, respectively. These present results indicate that CH-stretching first, combination band and second overtone regions can serve as diagnostic markers for gastric cancer.

  19. Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

    Science.gov (United States)

    Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

    2006-08-01

    This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

  20. Metodologia AGOA: a modelagem de clusters de hidratação no complexo aziridina···ácido fluorídrico AGOA methodology: modeling the hydration clusters for the aziridine···hydrofluoric complex

    Directory of Open Access Journals (Sweden)

    Boaz G. Oliveira

    2009-01-01

    Full Text Available We present a theoretical study of solvent effect on C2H5N···HF hydrogen-bonded complex through the application of the AGOA methodology. By using the TIP4P model to orientate the configuration of water molecules, the hydration clusters generated by AGOA were obtained through the analysis of the molecular electrostatic potential (MEP of solute (C2H5N···HF. Thereby, it was calculated the hydration energies on positive and negative MEP fields, which are maxima (PEMmax and minima (PEMmin when represent the -CH2- methylene groups and hydrofluoric acid, respectively. By taking into account the higher and lower hydration energy values of -370.6 kJ mol-1 and -74.3 kJ mol-1 for PEMmax and PEMmin of the C2H5N···HF, our analysis shows that these results corroborate the open ring reaction of aziridine, in which the preferential attack of water molecules occurs at the methylene groups of this heterocyclic.

  1. A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

    Science.gov (United States)

    Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

    2018-01-01

    This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

  2. Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

    Science.gov (United States)

    Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

    2017-12-01

    The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

  3. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

    OpenAIRE

    Qing Ye; Hao Pan; Changhua Liu

    2015-01-01

    A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA)...

  4. Consensus clustering approach to group brain connectivity matrices

    Directory of Open Access Journals (Sweden)

    Javier Rasero

    2017-10-01

    Full Text Available A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (a define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (b cluster the distance matrix for each node; (c build the consensus network from the corresponding partitions; and (d extract groups of subjects by finding the communities of the consensus network thus obtained. Different from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pretraining step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real datasets show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix.

  5. Subspace K-means clustering.

    Science.gov (United States)

    Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

    2013-12-01

    To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

  6. Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

    Science.gov (United States)

    Sarparandeh, Mohammadali; Hezarkhani, Ardeshir

    2017-12-01

    The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) - including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and self-organizing map (SOM) - were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed

  7. Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings

    OpenAIRE

    Aldarmaki, Hanan; Mohan, Mahesh; Diab, Mona

    2017-01-01

    Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector ...

  8. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    Science.gov (United States)

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  9. Information-Based Approach to Unsupervised Machine Learning

    Science.gov (United States)

    2013-06-19

    samples with large fitting error. The above optimization problem can be reduced to a quadratic program (Mangasarian & Musicant , 2000), which can be...recognition. Technicheskaya Kibernetica, 3. in Russian. Mallows, C. L. (1973). Some comments on CP . Technometrics, 15, 661–675. Mangasarian, O. L., & Musicant ...to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summa- rization is a typical application

  10. Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks

    OpenAIRE

    Zhelezniak, Vitalii; Busbridge, Dan; Shen, April; Smith, Samuel L.; Hammerla, Nils Y.

    2018-01-01

    Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space, in which semantically close symbols are mapped to representations that are close under a similarity measure induced by the model's objective function. In addition, we present a straightforward procedure that, without any retraining or architectura...

  11. Unsupervised Learning of Spatiotemporal Features by Video Completion

    OpenAIRE

    Nallabolu, Adithya Reddy

    2017-01-01

    In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per...

  12. A new web-based system for unsupervised classification of satellite images from the Google Maps engine

    Science.gov (United States)

    Ferrán, Ángel; Bernabé, Sergio; García-Rodríguez, Pablo; Plaza, Antonio

    2012-10-01

    In this paper, we develop a new web-based system for unsupervised classification of satellite images available from the Google Maps engine. The system has been developed using the Google Maps API and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the desired zoom level). For this purpose, we use a processing chain made up of the well-known ISODATA and k-means algorithms, followed by spatial post-processing based on majority voting. The system is currently hosted on a high performance server which performs the execution of classification algorithms and returns the obtained classification results in a very efficient way. The previous functionalities are necessary to use efficient techniques for the classification of images and the incorporation of content-based image retrieval (CBIR). Several experimental validation types of the classification results with the proposed system are performed by comparing the classification accuracy of the proposed chain by means of techniques available in the well-known Environment for Visualizing Images (ENVI) software package. The server has access to a cluster of commodity graphics processing units (GPUs), hence in future work we plan to perform the processing in parallel by taking advantage of the cluster.

  13. Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents

    Directory of Open Access Journals (Sweden)

    Nicole eCichon

    2014-05-01

    Full Text Available Flexible communication within the brain, which relies on oscillatory activity, is not confined to adult neuronal networks. Experimental evidence has documented the presence of discontinuous patterns of oscillatory activity already during early development. Their highly variable spatial and time-frequency organization has been related to region specificity. However, it might be equally due to the absence of unitary criteria for classifying the early activity patterns, since they have been mainly characterized by visual inspection. Therefore, robust and unbiased methods for categorizing these discontinuous oscillations are needed for increasingly complex data sets from different labs. Here, we introduce an unsupervised detection and classification algorithm for the discontinuous activity patterns of rodents during early development. For this, firstly time windows with discontinuous oscillations vs. epochs of network silence were identified. In a second step, the major features of detected events were identified and processed by principal component analysis for deciding on their contribution to the classification of different oscillatory patterns. Finally, these patterns were categorized using an unsupervised cluster algorithm. The results were validated on manually characterized neonatal spindle bursts, which ubiquitously entrain neocortical areas of rats and mice, and prelimbic nested gamma spindle bursts. Moreover, the algorithm led to satisfactory results for oscillatory events that, due to increased similarity of their features, were more difficult to classify, e.g. during the pre-juvenile developmental period. Based on a linear classification, the optimal number of features to consider increased with the difficulty of detection. This algorithm allows the comparison of neonatal and pre-juvenile oscillatory patterns in their spatial and temporal organization. It might represent a first step for the unbiased elucidation of activity patterns

  14. Automatic Clustering Using FSDE-Forced Strategy Differential Evolution

    Science.gov (United States)

    Yasid, A.

    2018-01-01

    Clustering analysis is important in datamining for unsupervised data, cause no adequate prior knowledge. One of the important tasks is defining the number of clusters without user involvement that is known as automatic clustering. This study intends on acquiring cluster number automatically utilizing forced strategy differential evolution (AC-FSDE). Two mutation parameters, namely: constant parameter and variable parameter are employed to boost differential evolution performance. Four well-known benchmark datasets were used to evaluate the algorithm. Moreover, the result is compared with other state of the art automatic clustering methods. The experiment results evidence that AC-FSDE is better or competitive with other existing automatic clustering algorithm.

  15. Clustering and visualizing similarity networks of membrane proteins.

    Science.gov (United States)

    Hu, Geng-Ming; Mai, Te-Lun; Chen, Chi-Ming

    2015-08-01

    We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

  16. Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System

    Science.gov (United States)

    Lucena, Caroline Vieira; Lacerda, Marcelo; Caldas, Rafael; De Lima Neto, Fernando Buarque

    2018-01-01

    There is a direct relationship between the prevalence of musculoskeletal disorders of the temporomandibular joint and orofacial disorders. A well-elaborated analysis of the jaw movements provides relevant information for healthcare professionals to conclude their diagnosis. Different approaches have been explored to track jaw movements such that the mastication analysis is getting less subjective; however, all methods are still highly subjective, and the quality of the assessments depends much on the experience of the health professional. In this paper, an accurate and non-invasive method based on a commercial low-cost inertial sensor (MPU6050) to measure jaw movements is proposed. The jaw-movement feature values are compared to the obtained with clinical analysis, showing no statistically significant difference between both methods. Moreover, We propose to use unsupervised paradigm approaches to cluster mastication patterns of healthy subjects and simulated patients with facial trauma. Two techniques were used in this paper to instantiate the method: Kohonen’s Self-Organizing Maps and K-Means Clustering. Both algorithms have excellent performances to process jaw-movements data, showing encouraging results and potential to bring a full assessment of the masticatory function. The proposed method can be applied in real-time providing relevant dynamic information for health-care professionals. PMID:29651365

  17. A new avenue for classification and prediction of olive cultivars using supervised and unsupervised algorithms.

    Directory of Open Access Journals (Sweden)

    Amir H Beiki

    Full Text Available Various methods have been used to identify cultivares of olive trees; herein we used different bioinformatics algorithms to propose new tools to classify 10 cultivares of olive based on RAPD and ISSR genetic markers datasets generated from PCR reactions. Five RAPD markers (OPA0a21, OPD16a, OP01a1, OPD16a1 and OPA0a8 and five ISSR markers (UBC841a4, UBC868a7, UBC841a14, U12BC807a and UBC810a13 selected as the most important markers by all attribute weighting models. K-Medoids unsupervised clustering run on SVM dataset was fully able to cluster each olive cultivar to the right classes. All trees (176 induced by decision tree models generated meaningful trees and UBC841a4 attribute clearly distinguished between foreign and domestic olive cultivars with 100% accuracy. Predictive machine learning algorithms (SVM and Naïve Bayes were also able to predict the right class of olive cultivares with 100% accuracy. For the first time, our results showed data mining techniques can be effectively used to distinguish between plant cultivares and proposed machine learning based systems in this study can predict new olive cultivars with the best possible accuracy.

  18. Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach

    Directory of Open Access Journals (Sweden)

    Sebastian Böttcher

    2018-03-01

    Full Text Available Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user’s physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods.

  19. Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System.

    Science.gov (United States)

    Lucena, Caroline Vieira; Lacerda, Marcelo; Caldas, Rafael; De Lima Neto, Fernando Buarque; Rativa, Diego

    2018-01-01

    There is a direct relationship between the prevalence of musculoskeletal disorders of the temporomandibular joint and orofacial disorders. A well-elaborated analysis of the jaw movements provides relevant information for healthcare professionals to conclude their diagnosis. Different approaches have been explored to track jaw movements such that the mastication analysis is getting less subjective; however, all methods are still highly subjective, and the quality of the assessments depends much on the experience of the health professional. In this paper, an accurate and non-invasive method based on a commercial low-cost inertial sensor (MPU6050) to measure jaw movements is proposed. The jaw-movement feature values are compared to the obtained with clinical analysis, showing no statistically significant difference between both methods. Moreover, We propose to use unsupervised paradigm approaches to cluster mastication patterns of healthy subjects and simulated patients with facial trauma. Two techniques were used in this paper to instantiate the method: Kohonen's Self-Organizing Maps and K-Means Clustering. Both algorithms have excellent performances to process jaw-movements data, showing encouraging results and potential to bring a full assessment of the masticatory function. The proposed method can be applied in real-time providing relevant dynamic information for health-care professionals.

  20. Semi-supervised clustering methods.

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

  1. Analog memristive synapse in spiking networks implementing unsupervised learning

    Directory of Open Access Journals (Sweden)

    Erika Covi

    2016-10-01

    Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

  2. Automated and unsupervised detection of malarial parasites in microscopic images

    Directory of Open Access Journals (Sweden)

    Purwar Yashasvi

    2011-12-01

    Full Text Available Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis and prone to human error (leading to erroneous diagnosis, even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. Method A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. Results The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Conclusion Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method

  3. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

    Science.gov (United States)

    Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

    2016-01-01

    Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO 2 -based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

  4. Unsupervised classification of operator workload from brain signals

    Science.gov (United States)

    Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

    2016-06-01

    Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

  5. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Kamronn, Simon Due; Paquet, Ulrich

    2017-01-01

    This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework...... for unsupervised learning of sequential data that disentangles two latent representations: an object’s representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate...

  6. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  7. Unsupervised learning via self-organization a dynamic approach

    CERN Document Server

    Kyan, Matthew; Jarrah, Kambiz; Guan, Ling

    2014-01-01

    To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

  8. Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

    Czech Academy of Sciences Publication Activity Database

    Scarpa, G.; Gaetano, R.; Haindl, Michal; Zerubia, J.

    2009-01-01

    Roč. 18, č. 8 (2009), s. 1830-1843 ISSN 1057-7149 R&D Projects: GA ČR GA102/08/0593 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : Classification * texture analysis * segmentation * hierarchical image models * Markov process Subject RIV: BD - Theory of Information Impact factor: 2.848, year: 2009 http://library.utia.cas.cz/separaty/2009/RO/haindl-hierarchical multiple markov chain model for unsupervised texture segmentation.pdf

  9. Unsupervised detection of salt marsh platforms: a topographic method

    Science.gov (United States)

    Goodwin, Guillaume C. H.; Mudd, Simon M.; Clubb, Fiona J.

    2018-03-01

    Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labour-intensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM), referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives and overall platform

  10. Unsupervised detection of salt marsh platforms: a topographic method

    Directory of Open Access Journals (Sweden)

    G. C. H. Goodwin

    2018-03-01

    Full Text Available Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labour-intensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM, referred to as Topographic Identification of Platforms (TIP. Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives

  11. Classification Of Cluster Area Forsatellite Image

    Directory of Open Access Journals (Sweden)

    Thwe Zin Phyo

    2015-06-01

    Full Text Available Abstract This paper describes area classification for Landsat7 satellite image. The main purpose of this system is to classify the area of each cluster contained in a satellite image. To classify this image firstly need to clusterthe satellite image into different land cover types. Clustering is an unsupervised learning method that aimsto classify an image into homogeneous regions. This system is implemented based on color features with K-means clustering unsupervised algorithm. This method does not need to train image before clustering.The clusters of satellite image are grouped into a set of three clusters for Landsat7 satellite image. For this work the combined band 432 from Landsat7 satellite is used as an input. Satellite imageMandalay area in 2001 is chosen to test the segmentation method. After clustering a specific range for three clustered images must be defined in order to obtain greenland water and urbanbalance.This system is implemented by using MATLAB programming language.

  12. Machine learning in APOGEE. Unsupervised spectral classification with K-means

    Science.gov (United States)

    Garcia-Dias, Rafael; Allende Prieto, Carlos; Sánchez Almeida, Jorge; Ordovás-Pascual, Ignacio

    2018-05-01

    Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic Evolution Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra, which is perfect for testing such alternatives. Aims: Our research applies an unsupervised classification scheme based on K-means to the massive APOGEE data set. We explore whether the data are amenable to classification into discrete classes. Methods: We apply the K-means algorithm to 153 847 high resolution spectra (R ≈ 22 500). We discuss the main virtues and weaknesses of the algorithm, as well as our choice of parameters. Results: We show that a classification based on normalised spectra captures the variations in stellar atmospheric parameters, chemical abundances, and rotational velocity, among other factors. The algorithm is able to separate the bulge and halo populations, and distinguish dwarfs, sub-giants, RC, and RGB stars. However, a discrete classification in flux space does not result in a neat organisation in the parameters' space. Furthermore, the lack of obvious groups in flux space causes the results to be fairly sensitive to the initialisation, and disrupts the efficiency of commonly-used methods to select the optimal number of clusters. Our classification is publicly available, including extensive online material associated with the APOGEE Data Release 12 (DR12). Conclusions: Our description of the APOGEE database can help greatly with the identification of specific types of targets for various applications. We find a lack of obvious groups in flux space, and identify limitations of the K-means algorithm in dealing with this kind of data. Full Tables B.1-B.4 are only available at the CDS via

  13. An improved clustering algorithm based on reverse learning in intelligent transportation

    Science.gov (United States)

    Qiu, Guoqing; Kou, Qianqian; Niu, Ting

    2017-05-01

    With the development of artificial intelligence and data mining technology, big data has gradually entered people's field of vision. In the process of dealing with large data, clustering is an important processing method. By introducing the reverse learning method in the clustering process of PAM clustering algorithm, to further improve the limitations of one-time clustering in unsupervised clustering learning, and increase the diversity of clustering clusters, so as to improve the quality of clustering. The algorithm analysis and experimental results show that the algorithm is feasible.

  14. Semi-supervised clustering methods

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

  15. Statistical Significance for Hierarchical Clustering

    Science.gov (United States)

    Kimes, Patrick K.; Liu, Yufeng; Hayes, D. Neil; Marron, J. S.

    2017-01-01

    Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets. PMID:28099990

  16. Remote photoplethysmography system for unsupervised monitoring regional anesthesia effectiveness

    Science.gov (United States)

    Rubins, U.; Miscuks, A.; Marcinkevics, Z.; Lange, M.

    2017-12-01

    Determining the level of regional anesthesia (RA) is vitally important to both an anesthesiologist and surgeon, also knowing the RA level can protect the patient and reduce the time of surgery. Normally to detect the level of RA, usually a simple subjective (sensitivity test) and complicated quantitative methods (thermography, neuromyography, etc.) are used, but there is not yet a standardized method for objective RA detection and evaluation. In this study, the advanced remote photoplethysmography imaging (rPPG) system for unsupervised monitoring of human palm RA is demonstrated. The rPPG system comprises compact video camera with green optical filter, surgical lamp as a light source and a computer with custom-developed software. The algorithm implemented in Matlab software recognizes the palm and two dermatomes (Medial and Ulnar innervation), calculates the perfusion map and perfusion changes in real-time to detect effect of RA. Seven patients (aged 18-80 years) undergoing hand surgery received peripheral nerve brachial plexus blocks during the measurements. Clinical experiments showed that our rPPG system is able to perform unsupervised monitoring of RA.

  17. An unsupervised adaptive strategy for constructing probabilistic roadmaps

    KAUST Repository

    Tapia, L.

    2009-05-01

    Since planning environments are complex and no single planner exists that is best for all problems, much work has been done to explore methods for selecting where and when to apply particular planners. However, these two questions have been difficult to answer, even when adaptive methods meant to facilitate a solution are applied. For example, adaptive solutions such as setting learning rates, hand-classifying spaces, and defining parameters for a library of planners have all been proposed. We demonstrate a strategy based on unsupervised learning methods that makes adaptive planning more practical. The unsupervised strategies require less user intervention, model the topology of the problem in a reasonable and efficient manner, can adapt the sampler depending on characteristics of the problem, and can easily accept new samplers as they become available. Through a series of experiments, we demonstrate that in a wide variety of environments, the regions automatically identified by our technique represent the planning space well both in number and placement.We also show that our technique has little overhead and that it out-performs two existing adaptive methods in all complex cases studied.© 2009 IEEE.

  18. An automatic taxonomy of galaxy morphology using unsupervised machine learning

    Science.gov (United States)

    Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

    2018-01-01

    We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

  19. Function approximation using combined unsupervised and supervised learning.

    Science.gov (United States)

    Andras, Peter

    2014-03-01

    Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

  20. Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

    Science.gov (United States)

    Hunt, B.; Sheppard, D. G.; Wetterer, C. J.

    There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.

  1. Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts

    NARCIS (Netherlands)

    Castaldi, Peter J; Benet, Marta; Petersen, Hans; Rafaels, Nicholas; Finigan, James; Paoletti, Matteo; Marike Boezen, H; Vonk, Judith M; Bowler, Russell; Pistolesi, Massimo; Puhan, Milo A; Anto, Josep; Wauters, Els; Lambrechts, Diether; Janssens, Wim; Bigazzi, Francesca; Camiciottoli, Gianna; Cho, Michael H; Hersh, Craig P; Barnes, Kathleen; Rennard, Stephen; Boorgula, Meher Preethi; Dy, Jennifer; Hansel, Nadia N; Crapo, James D; Tesfaigzi, Yohannes; Agusti, Alvar; Silverman, Edwin K; Garcia-Aymerich, Judith

    Background COPD is a heterogeneous disease, but there is little consensus on specific definitions for COPD subtypes. Unsupervised clustering offers the promise of 'unbiased' data-driven assessment of COPD heterogeneity. Multiple groups have identified COPD subtypes using cluster analysis, but there

  2. Semi-Supervised Clustering for High-Dimensional and Sparse Features

    Science.gov (United States)

    Yan, Su

    2010-01-01

    Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

  3. Unsupervised Categorization in a Sample of Children with Autism Spectrum Disorders

    Science.gov (United States)

    Edwards, Darren J.; Perlman, Amotz; Reed, Phil

    2012-01-01

    Studies of supervised Categorization have demonstrated limited Categorization performance in participants with autism spectrum disorders (ASD), however little research has been conducted regarding unsupervised Categorization in this population. This study explored unsupervised Categorization using two stimulus sets that differed in their…

  4. Clustering of near clusters versus cluster compactness

    International Nuclear Information System (INIS)

    Yu Gao; Yipeng Jing

    1989-01-01

    The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)

  5. Random clustering ferns for multimodal object recognition

    OpenAIRE

    Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc

    2017-01-01

    The final publication is available at link.springer.com We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficient...

  6. Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

    Science.gov (United States)

    de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

    2015-06-01

    Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

  7. Unsupervised machine learning account of magnetic transitions in the Hubbard model

    Science.gov (United States)

    Ch'ng, Kelvin; Vazquez, Nick; Khatami, Ehsan

    2018-01-01

    We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and t -distributed stochastic neighboring ensemble (t -SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary) spin configurations generated, through Monte Carlo simulations of small clusters, for the Ising and Fermi-Hubbard models at finite temperatures. Results from a convolutional autoencoder for the three-dimensional Ising model can be shown to produce the magnetization and the susceptibility as a function of temperature with a high degree of accuracy. Quantum fluctuations distort this picture and prevent us from making such connections between the output of the autoencoder and physical observables for the Hubbard model. However, we are able to define an indicator based on the output of the t -SNE algorithm that shows a near perfect agreement with the antiferromagnetic structure factor of the model in two and three spatial dimensions in the weak-coupling regime. t -SNE also predicts a transition to the canted antiferromagnetic phase for the three-dimensional model when a strong magnetic field is present. We show that these techniques cannot be expected to work away from half filling when the "sign problem" in quantum Monte Carlo simulations is present.

  8. Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    V. Balaji

    2016-01-01

    Full Text Available In this paper we present a Cooperative Spectrum Sensing (CSS algorithm for Cognitive Radios (CR based on IEEE 802.22Wireless Regional Area Network (WRAN standard. The core objective is to improve cooperative sensing efficiency which specifies how fast a decision can be reached in each round of cooperation (iteration to sense an appropriate number of channels/bands (i.e. 86 channels of 7MHz bandwidth as per IEEE 802.22 within a time constraint (channel sensing time. To meet this objective, we have developed CSS algorithm using unsupervised K-means clustering classification approach. The received energy level of each Secondary User (SU is considered as the parameter for determining channel availability. The performance of proposed algorithm is quantified in terms of detection accuracy, training and classification delay time. Further, the detection accuracy of our proposed scheme meets the requirement of IEEE 802.22 WRAN with the target probability of falsealrm as 0.1. All the simulations are carried out using Matlab tool.

  9. An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

    Science.gov (United States)

    LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

    2016-12-01

    Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

  10. Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

    Science.gov (United States)

    Das, Anup; Pradhapan, Paruthi; Groenendaal, Willemijn; Adiraju, Prathyusha; Rajan, Raj Thilak; Catthoor, Francky; Schaafsma, Siebren; Krichmar, Jeffrey L; Dutt, Nikil; Van Hoof, Chris

    2018-03-01

    Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

    Directory of Open Access Journals (Sweden)

    Alim Samat

    2016-03-01

    Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

  12. Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement.

    Science.gov (United States)

    Moen, Hans; Peltonen, Laura-Maria; Koivumäki, Mikko; Suhonen, Henry; Salakoski, Tapio; Ginter, Filip; Salanterä, Sanna

    2018-01-01

    We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled text - a clinical corpus and a general domain corpus. A joint semantic word-space model is created for the purpose of extracting easier to understand alternatives for words considered difficult to understand by laymen. Two domain experts evaluate the tool and inter-rater agreement is calculated. When having the tool suggest ten alternatives to each difficult word, it suggests acceptable lay words for 55.51% of them. This and future manual evaluation will serve to further improve performance, where also supervised machine learning will be used.

  13. Application of unsupervised learning methods in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Koevesarki, Peter; Nuncio Quiroz, Adriana Elizabeth; Brock, Ian C. [Physikalisches Institut, Universitaet Bonn, Bonn (Germany)

    2011-07-01

    High energy physics is a home for a variety of multivariate techniques, mainly due to the fundamentally probabilistic behaviour of nature. These methods generally require training based on some theory, in order to discriminate a known signal from a background. Nevertheless, new physics can show itself in ways that previously no one thought about, and in these cases conventional methods give little or no help. A possible way to discriminate between known processes (like vector bosons or top-quark production) or look for new physics is using unsupervised machine learning to extract the features of the data. A technique was developed, based on the combination of neural networks and the method of principal curves, to find a parametrisation of the non-linear correlations of the data. The feasibility of the method is shown on ATLAS data.

  14. Unsupervised/supervised learning concept for 24-hour load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Babic, B [Electrical Power Industry of Serbia, Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science

    1993-07-01

    An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

  15. Unsupervised feature learning for autonomous rock image classification

    Science.gov (United States)

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

  16. Perceptual approach for unsupervised digital color restoration of cinematographic archives

    Science.gov (United States)

    Chambah, Majed; Rizzi, Alessandro; Gatta, Carlo; Besserer, Bernard; Marini, Daniele

    2003-01-01

    The cinematographic archives represent an important part of our collective memory. We present in this paper some advances in automating the color fading restoration process, especially with regard to the automatic color correction technique. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. There are some advantages in a perceptual approach: mainly its robustness and its local filtering properties, that lead to more effective results. The resulting technique, is not just an application of ACE on movie images, but an enhancement of ACE principles to meet the requirements in the digital film restoration field. The presented preliminary results are satisfying and promising.

  17. Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

    Science.gov (United States)

    Orbán, Levente L; Chartier, Sylvain

    2015-01-01

    Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

  18. Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

    Science.gov (United States)

    Hu, Wei; Lv, Jiancheng; Liu, Dongbo; Chen, Yao

    2018-04-01

    Cardiovascular disease seriously threatens the health of many people. It is usually diagnosed during cardiac auscultation, which is a fast and efficient method of cardiovascular disease diagnosis. In recent years, deep learning approach using unsupervised learning has made significant breakthroughs in many fields. However, to our knowledge, deep learning has not yet been used for heart sound classification. In this paper, we first use the average Shannon energy to extract the envelope of the heart sounds, then find the highest point of S1 to extract the cardiac cycle. We convert the time-domain signals of the cardiac cycle into spectrograms and apply principal component analysis whitening to reduce the dimensionality of the spectrogram. Finally, we apply a two-layer autoencoder to extract the features of the spectrogram. The experimental results demonstrate that the features from the autoencoder are suitable for heart sound classification.

  19. CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

    2017-07-14

    We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

  20. Unsupervised online classifier in sleep scoring for sleep deprivation studies.

    Science.gov (United States)

    Libourel, Paul-Antoine; Corneyllie, Alexandra; Luppi, Pierre-Hervé; Chouvet, Guy; Gervasoni, Damien

    2015-05-01

    This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings. The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method. © 2015 Associated Professional Sleep Societies, LLC.

  1. Surface mapping via unsupervised classification of remote sensing: application to MESSENGER/MASCS and DAWN/VIRS data.

    Science.gov (United States)

    D'Amore, M.; Le Scaon, R.; Helbert, J.; Maturilli, A.

    2017-12-01

    Machine-learning achieved unprecedented results in high-dimensional data processing tasks with wide applications in various fields. Due to the growing number of complex nonlinear systems that have to be investigated in science and the bare raw size of data nowadays available, ML offers the unique ability to extract knowledge, regardless the specific application field. Examples are image segmentation, supervised/unsupervised/ semi-supervised classification, feature extraction, data dimensionality analysis/reduction.The MASCS instrument has mapped Mercury surface in the 400-1145 nm wavelength range during orbital observations by the MESSENGER spacecraft. We have conducted k-means unsupervised hierarchical clustering to identify and characterize spectral units from MASCS observations. The results display a dichotomy: a polar and equatorial units, possibly linked to compositional differences or weathering due to irradiation. To explore possible relations between composition and spectral behavior, we have compared the spectral provinces with elemental abundance maps derived from MESSENGER's X-Ray Spectrometer (XRS).For the Vesta application on DAWN Visible and infrared spectrometer (VIR) data, we explored several Machine Learning techniques: image segmentation method, stream algorithm and hierarchical clustering.The algorithm successfully separates the Olivine outcrops around two craters on Vesta's surface [1]. New maps summarizing the spectral and chemical signature of the surface could be automatically produced.We conclude that instead of hand digging in data, scientist could choose a subset of algorithms with well known feature (i.e. efficacy on the particular problem, speed, accuracy) and focus their effort in understanding what important characteristic of the groups found in the data mean. [1] E Ammannito et al. "Olivine in an unexpected location on Vesta's surface". In: Nature 504.7478 (2013), pp. 122-125.

  2. Deep supervised, but not unsupervised, models may explain IT cortical representation.

    Directory of Open Access Journals (Sweden)

    Seyed-Mahdi Khaligh-Razavi

    2014-11-01

    Full Text Available Inferior temporal (IT cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total, testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network. We compared the representational dissimilarity matrices (RDMs of the model representations with the RDMs obtained from human IT (measured with fMRI and monkey IT (measured with cell recording for the same set of stimuli (not used in training the models. Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining

  3. Semi-Supervised Generation with Cluster-aware Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Fraccaro, Marco; Winther, Ole

    2017-01-01

    Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Clust...... a log-likelihood of −79.38 nats on permutation invariant MNIST, while also achieving competitive semi-supervised classification accuracies. The model can also be trained fully unsupervised, and still improve the log-likelihood performance with respect to related methods.......Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster...

  4. A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

    Directory of Open Access Journals (Sweden)

    Fateh Nassim Melzi

    2017-09-01

    Full Text Available The large amount of data collected by smart meters is a valuable resource that can be used to better understand consumer behavior and optimize electricity consumption in cities. This paper presents an unsupervised classification approach for extracting typical consumption patterns from data generated by smart electric meters. The proposed approach is based on a constrained Gaussian mixture model whose parameters vary according to the day type (weekday, Saturday or Sunday. The proposed methodology is applied to a real dataset of Irish households collected by smart meters over one year. For each cluster, the model provides three consumption profiles that depend on the day type. In the first instance, the model is applied on the electricity consumption of users during one month to extract groups of consumers who exhibit similar consumption behaviors. The clustering results are then crossed with contextual variables available for the households to show the close links between electricity consumption and household socio-economic characteristics. At the second instance, the evolution of the consumer behavior from one month to another is assessed through variations of cluster sizes over time. The results show that the consumer behavior evolves over time depending on the contextual variables such as temperature fluctuations and calendar events.

  5. Community-based intermittent mass testing and treatment for malaria in an area of high transmission intensity, western Kenya: study design and methodology for a cluster randomized controlled trial.

    Science.gov (United States)

    Samuels, Aaron M; Awino, Nobert; Odongo, Wycliffe; Abong'o, Benard; Gimnig, John; Otieno, Kephas; Shi, Ya Ping; Were, Vincent; Allen, Denise Roth; Were, Florence; Sang, Tony; Obor, David; Williamson, John; Hamel, Mary J; Patrick Kachur, S; Slutsker, Laurence; Lindblade, Kim A; Kariuki, Simon; Desai, Meghna

    2017-06-07

    Most human Plasmodium infections in western Kenya are asymptomatic and are believed to contribute importantly to malaria transmission. Elimination of asymptomatic infections requires active treatment approaches, such as mass testing and treatment (MTaT) or mass drug administration (MDA), as infected persons do not seek care for their infection. Evaluations of community-based approaches that are designed to reduce malaria transmission require careful attention to study design to ensure that important effects can be measured accurately. This manuscript describes the study design and methodology of a cluster-randomized controlled trial to evaluate a MTaT approach for malaria transmission reduction in an area of high malaria transmission. Ten health facilities in western Kenya were purposively selected for inclusion. The communities within 3 km of each health facility were divided into three clusters of approximately equal population size. Two clusters around each health facility were randomly assigned to the control arm, and one to the intervention arm. Three times per year for 2 years, after the long and short rains, and again before the long rains, teams of community health volunteers visited every household within the intervention arm, tested all consenting individuals with malaria rapid diagnostic tests, and treated all positive individuals with an effective anti-malarial. The effect of mass testing and treatment on malaria transmission was measured through population-based longitudinal cohorts, outpatient visits for clinical malaria, periodic population-based cross-sectional surveys, and entomological indices.

  6. Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

    Directory of Open Access Journals (Sweden)

    Ujjwal Maulik

    Full Text Available Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.sarkar@labri.fr.

  7. Least-squares gradient calculation from multi-point observations of scalar and vector fields: methodology and applications with Cluster in the plasmasphere

    Directory of Open Access Journals (Sweden)

    J. De Keyser

    2007-05-01

    Full Text Available This paper describes a general-purpose algorithm for computing the gradients in space and time of a scalar field, a vector field, or a divergence-free vector field, from in situ measurements by one or more spacecraft. The algorithm provides total error estimates on the computed gradient, including the effects of measurement errors, the errors due to a lack of spatio-temporal homogeneity, and errors due to small-scale fluctuations. It also has the ability to diagnose the conditioning of the problem. Optimal use is made of the data, in terms of exploiting the maximum amount of information relative to the uncertainty on the data, by solving the problem in a weighted least-squares sense. The method is illustrated using Cluster magnetic field and electron density data to compute various gradients during a traversal of the inner magnetosphere. In particular, Cluster is shown to cross azimuthal density structure, and the existence of field-aligned currents in the plasmasphere is demonstrated.

  8. Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

    Science.gov (United States)

    2015-01-01

    A. Porter and my advisor. The text is primarily written by me. Chapter 5 is a version of [46] where my contribution is all of the analytical ...inn Euclidean space, a variational method refers to using calculus of variation techniques to find the minimizer (or maximizer) of a functional (energy... geometric inter- pretation of modularity optimization contrasts with existing interpretations (e.g., probabilistic ones or in terms of the Potts model

  9. Smart Fog: Fog Computing Framework for Unsupervised Clustering Analytics in Wearable Internet of Things

    OpenAIRE

    Borthakur, Debanjan; Dubey, Harishchandra; Constant, Nicholas; Mahler, Leslie; Mankodiya, Kunal

    2017-01-01

    The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state of the art telecare systems, the signal processing and machine learning modules are deployed in the clou...

  10. Supervised and Unsupervised Speaker Adaptation in the NIST 2005 Speaker Recognition Evaluation

    National Research Council Canada - National Science Library

    Hansen, Eric G; Slyh, Raymond E; Anderson, Timothy R

    2006-01-01

    Starting in 2004, the annual NIST Speaker Recognition Evaluation (SRE) has added an optional unsupervised speaker adaptation track where test files are processed sequentially and one may update the target model...

  11. Algorithms of maximum likelihood data clustering with applications

    Science.gov (United States)

    Giada, Lorenzo; Marsili, Matteo

    2002-12-01

    We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

  12. Inferring hierarchical clustering structures by deterministic annealing

    International Nuclear Information System (INIS)

    Hofmann, T.; Buhmann, J.M.

    1996-01-01

    The unsupervised detection of hierarchical structures is a major topic in unsupervised learning and one of the key questions in data analysis and representation. We propose a novel algorithm for the problem of learning decision trees for data clustering and related problems. In contrast to many other methods based on successive tree growing and pruning, we propose an objective function for tree evaluation and we derive a non-greedy technique for tree growing. Applying the principles of maximum entropy and minimum cross entropy, a deterministic annealing algorithm is derived in a meanfield approximation. This technique allows us to canonically superimpose tree structures and to fit parameters to averaged or open-quote fuzzified close-quote trees

  13. Unsupervised laparoscopic appendicectomy by surgical trainees is safe and time-effective.

    Science.gov (United States)

    Wong, Kenneth; Duncan, Tristram; Pearson, Andrew

    2007-07-01

    Open appendicectomy is the traditional standard treatment for appendicitis. Laparoscopic appendicectomy is perceived as a procedure with greater potential for complications and longer operative times. This paper examines the hypothesis that unsupervised laparoscopic appendicectomy by surgical trainees is a safe and time-effective valid alternative. Medical records, operating theatre records and histopathology reports of all patients undergoing laparoscopic and open appendicectomy over a 15-month period in two hospitals within an area health service were retrospectively reviewed. Data were analysed to compare patient features, pathology findings, operative times, complications, readmissions and mortality between laparoscopic and open groups and between unsupervised surgical trainee operators versus consultant surgeon operators. A total of 143 laparoscopic and 222 open appendicectomies were reviewed. Unsupervised trainees performed 64% of the laparoscopic appendicectomies and 55% of the open appendicectomies. There were no significant differences in complication rates, readmissions, mortality and length of stay between laparoscopic and open appendicectomy groups or between trainee and consultant surgeon operators. Conversion rates (laparoscopic to open approach) were similar for trainees and consultants. Unsupervised senior surgical trainees did not take significantly longer to perform laparoscopic appendicectomy when compared to unsupervised trainee-performed open appendicectomy. Unsupervised laparoscopic appendicectomy by surgical trainees is safe and time-effective.

  14. Indoor localization using unsupervised manifold alignment with geometry perturbation

    KAUST Repository

    Majeed, Khaqan

    2014-04-01

    The main limitation of deploying/updating Received Signal Strength (RSS) based indoor localization is the construction of fingerprinted radio map, which is quite a hectic and time-consuming process especially when the indoor area is enormous and/or dynamic. Different approaches have been undertaken to reduce such deployment/update efforts, but the performance degrades when the fingerprinting load is reduced below a certain level. In this paper, we propose an indoor localization scheme that requires as low as 1% fingerprinting load. This scheme employs unsupervised manifold alignment that takes crowd sourced RSS readings and localization requests as source data set and the environment\\'s plan coordinates as destination data set. The 1% fingerprinting load is only used to perturb the local geometries in the destination data set. Our proposed algorithm was shown to achieve less than 5 m mean localization error with 1% fingerprinting load and a limited number of crowd sourced readings, when other learning based localization schemes pass the 10 m mean error with the same information.

  15. An unsupervised method for summarizing egocentric sport videos

    Science.gov (United States)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    People are getting more interested to record their sport activities using head-worn or hand-held cameras. This type of videos which is called egocentric sport videos has different motion and appearance patterns compared with life-logging videos. While a life-logging video can be defined in terms of well-defined human-object interactions, notwithstanding, it is not trivial to describe egocentric sport videos using well-defined activities. For this reason, summarizing egocentric sport videos based on human-object interaction might fail to produce meaningful results. In this paper, we propose an unsupervised method for summarizing egocentric videos by identifying the key-frames of the video. Our method utilizes both appearance and motion information and it automatically finds the number of the key-frames. Our blind user study on the new dataset collected from YouTube shows that in 93:5% cases, the users choose the proposed method as their first video summary choice. In addition, our method is within the top 2 choices of the users in 99% of studies.

  16. Spike timing analysis in neural networks with unsupervised synaptic plasticity

    Science.gov (United States)

    Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

    2013-01-01

    The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

  17. Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

    Science.gov (United States)

    Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

    We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

  18. Unsupervised learning of binary vectors: A Gaussian scenario

    International Nuclear Information System (INIS)

    Copelli, Mauro; Van den Broeck, Christian

    2000-01-01

    We study a model of unsupervised learning where the real-valued data vectors are isotropically distributed, except for a single symmetry-breaking binary direction B(set-membership sign){-1,+1} N , onto which the projections have a Gaussian distribution. We show that a candidate vector J undergoing Gibbs learning in this discrete space, approaches the perfect match J=B exponentially. In addition to the second-order ''retarded learning'' phase transition for unbiased distributions, we show that first-order transitions can also occur. Extending the known result that the center of mass of the Gibbs ensemble has Bayes-optimal performance, we show that taking the sign of the components of this vector (clipping) leads to the vector with optimal performance in the binary space. These upper bounds are shown generally not to be saturated with the technique of transforming the components of a special continuous vector, except in asymptotic limits and in a special linear case. Simulations are presented which are in excellent agreement with the theoretical results. (c) 2000 The American Physical Society

  19. Unsupervised neural networks for solving Troesch's problem

    International Nuclear Information System (INIS)

    Raja Muhammad Asif Zahoor

    2014-01-01

    In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. (interdisciplinary physics and related areas of science and technology)

  20. Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

    Directory of Open Access Journals (Sweden)

    Levente L Orbán

    Full Text Available Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

  1. Using DEDICOM for completely unsupervised part-of-speech tagging.

    Energy Technology Data Exchange (ETDEWEB)

    Chew, Peter A.; Bader, Brett William; Rozovskaya, Alla (University of Illinois, Urbana, IL)

    2009-02-01

    A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schuetze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to estimate emission- and transition-probability matrices which can then be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the correlation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging.

  2. Indoor localization using unsupervised manifold alignment with geometry perturbation

    KAUST Repository

    Majeed, Khaqan; Sorour, Sameh; Al-Naffouri, Tareq Y.; Valaee, Shahrokh

    2014-01-01

    The main limitation of deploying/updating Received Signal Strength (RSS) based indoor localization is the construction of fingerprinted radio map, which is quite a hectic and time-consuming process especially when the indoor area is enormous and/or dynamic. Different approaches have been undertaken to reduce such deployment/update efforts, but the performance degrades when the fingerprinting load is reduced below a certain level. In this paper, we propose an indoor localization scheme that requires as low as 1% fingerprinting load. This scheme employs unsupervised manifold alignment that takes crowd sourced RSS readings and localization requests as source data set and the environment's plan coordinates as destination data set. The 1% fingerprinting load is only used to perturb the local geometries in the destination data set. Our proposed algorithm was shown to achieve less than 5 m mean localization error with 1% fingerprinting load and a limited number of crowd sourced readings, when other learning based localization schemes pass the 10 m mean error with the same information.

  3. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

    Directory of Open Access Journals (Sweden)

    Guoqi Qian

    2016-01-01

    Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.

  4. Unsupervised binning of environmental genomic fragments based on an error robust selection of l-mers.

    Science.gov (United States)

    Yang, Bin; Peng, Yu; Leung, Henry Chi-Ming; Yiu, Siu-Ming; Chen, Jing-Chi; Chin, Francis Yuk-Lun

    2010-04-16

    With the rapid development of genome sequencing techniques, traditional research methods based on the isolation and cultivation of microorganisms are being gradually replaced by metagenomics, which is also known as environmental genomics. The first step, which is still a major bottleneck, of metagenomics is the taxonomic characterization of DNA fragments (reads) resulting from sequencing a sample of mixed species. This step is usually referred as "binning". Existing binning methods are based on supervised or semi-supervised approaches which rely heavily on reference genomes of known microorganisms and phylogenetic marker genes. Due to the limited availability of reference genomes and the bias and instability of marker genes, existing binning methods may not be applicable in many cases. In this paper, we present an unsupervised binning method based on the distribution of a carefully selected set of l-mers (substrings of length l in DNA fragments). From our experiments, we show that our method can accurately bin DNA fragments with various lengths and relative species abundance ratios without using any reference and training datasets. Another feature of our method is its error robustness. The binning accuracy decreases by less than 1% when the sequencing error rate increases from 0% to 5%. Note that the typical sequencing error rate of existing commercial sequencing platforms is less than 2%. We provide a new and effective tool to solve the metagenome binning problem without using any reference datasets or markers information of any known reference genomes (species). The source code of our software tool, the reference genomes of the species for generating the test datasets and the corresponding test datasets are available at http://i.cs.hku.hk/~alse/MetaCluster/.

  5. Improved Performance of Unsupervised Method by Renovated K-Means

    OpenAIRE

    Ashok, P.; Nawaz, G. M Kadhar; Elayaraja, E.; Vadivel, V.

    2013-01-01

    Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis...

  6. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

    Science.gov (United States)

    Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

    2013-03-01

    Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

  7. Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis.

    Science.gov (United States)

    Lacroix, André; Hortobágyi, Tibor; Beurskens, Rainer; Granacher, Urs

    2017-11-01

    to the following modalities: period, frequency, volume, modalities of supervision (i.e., number of supervised/unsupervised sessions within the supervised or unsupervised training groups, respectively). Heterogeneity was computed using I 2 and χ 2 statistics. The methodological quality of the included studies was evaluated using the Physiotherapy Evidence Database scale. Our analyses revealed that in older adults, supervised balance/resistance training was superior compared with unsupervised balance/resistance training in improving measures of static steady-state balance (mean SMD bs  = 0.28, p = 0.39), dynamic steady-state balance (mean SMD bs  = 0.35, p = 0.02), proactive balance (mean SMD bs  = 0.24, p = 0.05), balance test batteries (mean SMD bs  = 0.53, p = 0.02), and measures of muscle strength/power (mean SMD bs  = 0.51, p = 0.04). Regarding the examined dose-response relationships, our analyses showed that a number of 10-29 additional supervised sessions in the supervised training groups compared with the unsupervised training groups resulted in the largest effects for static steady-state balance (mean SMD bs  = 0.35), dynamic steady-state balance (mean SMD bs  = 0.37), and muscle strength/power (mean SMD bs  = 1.12). Further, ≥30 additional supervised sessions in the supervised training groups were needed to produce the largest effects on proactive balance (mean SMD bs  = 0.30) and balance test batteries (mean SMD bs  = 0.77). Effects in favor of supervised programs were larger for studies that did not include any supervised sessions in their unsupervised programs (mean SMD bs : 0.28-1.24) compared with studies that implemented a few supervised sessions in their unsupervised programs (e.g., three supervised sessions throughout the entire intervention program; SMD bs : -0.06 to 0.41). The present findings have to be interpreted with caution because of the low number of eligible studies and the moderate methodological quality

  8. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  9. Electrocardiogram signal quality measures for unsupervised telehealth environments

    International Nuclear Information System (INIS)

    Redmond, S J; Xie, Y; Chang, D; Lovell, N H; Basilakis, J

    2012-01-01

    The use of telehealth paradigms for the remote management of patients suffering from chronic conditions has become more commonplace with the advancement of Internet connectivity and enterprise software systems. To facilitate clinicians in managing large numbers of telehealth patients, and in digesting the vast array of data returned from the remote monitoring environment, decision support systems in various guises are often utilized. The success of decision support systems in interpreting patient conditions from physiological data is dependent largely on the quality of these recorded data. This paper outlines an algorithm to determine the quality of single-lead electrocardiogram (ECG) recordings obtained from telehealth patients. Three hundred short ECG recordings were manually annotated to identify movement artifact, QRS locations and signal quality (discrete quality levels) by a panel of three experts, who then reconciled the annotation as a group to resolve any discrepancies. After applying a published algorithm to remove gross movement artifact, the proposed method was then applied to estimate the remaining ECG signal quality, using a Parzen window supervised statistical classifier model. The three-class classifier model, using a number of time-domain features and evaluated using cross validation, gave an accuracy in classifying signal quality of 78.7% (κ = 0.67) when using fully automated preprocessing algorithms to remove gross motion artifact and detect QRS locations. This is a similar level of accuracy to the reported human inter-scorer agreement when generating the gold standard annotation (accuracy = 70–89.3%, κ = 0.54–0.84). These results indicate that the assessment of the quality of single-lead ECG recordings, acquired in unsupervised telehealth environments, is entirely feasible and may help to promote the acceptance and utility of future decision support systems for remotely managing chronic disease conditions. (paper)

  10. Cyclist–motorist crash patterns in Denmark: A latent class clustering approach

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Prato, Carlo Giacomo

    2013-01-01

    to prioritize safety issues and to devise efficient preventive measures. Method: The current study focused on cyclist–motorist crashes that occurred in Denmark during the period between 2007 and 2011. To uncover crash patterns, the current analysis applied latent class clustering, an unsupervised probabilistic...

  11. A Comparison of Methods for Player Clustering via Behavioral Telemetry

    DEFF Research Database (Denmark)

    Drachen, Anders; Thurau, C.; Sifa, R.

    2013-01-01

    patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Nonnegative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations......, interpretation of the resulting categories in terms of actual play behavior can be difficult if not impossible. In this paper, a range of unsupervised techniques are applied together with Archetypal Analysis to develop behavioral clusters from playtime data of 70,014 World of Warcraft players, covering a five......The analysis of user behavior in digital games has been aided by the introduction of user telemetry in game development, which provides unprecedented access to quantitative data on user behavior from the installed game clients of the entire population of players. Player behavior telemetry datasets...

  12. Unsupervised Bayesian linear unmixing of gene expression microarrays.

    Science.gov (United States)

    Bazot, Cécile; Dobigeon, Nicolas; Tourneret, Jean-Yves; Zaas, Aimee K; Ginsburg, Geoffrey S; Hero, Alfred O

    2013-03-19

    This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores

  13. Unsupervised topic modelling on South African parliament audio data

    CSIR Research Space (South Africa)

    Kleynhans, N

    2014-11-01

    Full Text Available Using a speech recognition system to convert spoken audio to text can enable the structuring of large collections of spoken audio data. A convenient means to summarise or cluster spoken data is to identify the topic under discussion. There are many...

  14. Thermodynamic free-energy minimization for unsupervised fusion of dual-color infrared breast images

    Science.gov (United States)

    Szu, Harold; Miao, Lidan; Qi, Hairong

    2006-04-01

    This paper presents algorithmic details of an unsupervised neural network and unbiased diagnostic methodology, that is, no lookup table is needed that labels the input training data with desired outputs. We deploy the smart algorithm on two satellite-grade infrared (IR) cameras. Although an early malignant tumor must be small in size and cannot be resolved by a single pixel that images about hundreds cells, these cells reveal themselves physiologically by emitting spontaneously thermal radiation due to the rapid cell growth angiogenesis effect (In Greek: vessels generation for increasing tumor blood supply), shifting toward, according to physics, a shorter IR wavelengths emission band. If we use those exceedingly sensitive IR spectral band cameras, we can in principle detect whether or not the breast tumor is perhaps malignant through a thin blouse in a close-up dark room. If this protocol turns out to be reliable in a large scale follow-on Vatican experiment in 2006, which might generate business investment interests of nano-engineering manufacture of nano-camera made of 1-D Carbon Nano-Tubes without traditional liquid Nitrogen coolant for Mid IR camera, then one can accumulate the probability of any type of malignant tumor at every pixel over time in the comfort of privacy without religious or other concerns. Such a non-intrusive protocol alone may not have enough information to make the decision, but the changes tracked over time will be surely becoming significant. Such an ill-posed inverse heat source transfer problem can be solved because of the universal constraint of equilibrium physics governing the blackbody Planck radiation distribution, to be spatio-temporally sampled. Thus, we must gather two snapshots with two IR cameras to form a vector data X(t) per pixel to invert the matrix-vector equation X=[A]S pixel-by-pixel independently, known as a single-pixel blind sources separation (BSS). Because the unknown heat transfer matrix or the impulse response

  15. Unsupervised Analysis of Array Comparative Genomic Hybridization Data from Early-Onset Colorectal Cancer Reveals Equivalence with Molecular Classification and Phenotypes

    Directory of Open Access Journals (Sweden)

    María Arriba

    2017-01-01

    Full Text Available AIM: To investigate whether chromosomal instability (CIN is associated with tumor phenotypes and/or with global genomic status based on MSI (microsatellite instability and CIMP (CpG island methylator phenotype in early-onset colorectal cancer (EOCRC. METHODS: Taking as a starting point our previous work in which tumors from 60 EOCRC cases (≤45 years at the time of diagnosis were analyzed by array comparative genomic hybridization (aCGH, in the present study we performed an unsupervised hierarchical clustering analysis of those aCGH data in order to unveil possible associations between the CIN profile and the clinical features of the tumors. In addition, we evaluated the MSI and the CIMP statuses of the samples with the aim of investigating a possible relationship between copy number alterations (CNAs and the MSI/CIMP condition in EOCRC. RESULTS: Based on the similarity of the CNAs detected, the unsupervised analysis stratified samples into two main clusters (A, B and four secondary clusters (A1, A2, B3, B4. The different subgroups showed a certain correspondence with the molecular classification of colorectal cancer (CRC, which enabled us to outline an algorithm to categorize tumors according to their CIMP status. Interestingly, each subcluster showed some distinctive clinicopathological features. But more interestingly, the CIN of each subcluster mainly affected particular chromosomes, allowing us to define chromosomal regions more specifically affected depending on the CIMP/MSI status of the samples. CONCLUSIONS: Our findings may provide a basis for a new form of classifying EOCRC according to the genomic status of the tumors.

  16. Correlates of Unsupervised Bathing of Infants: A Cross-Sectional Study

    Directory of Open Access Journals (Sweden)

    Tinneke M. J. Beirens

    2013-03-01

    Full Text Available Drowning represents the third leading cause of fatal unintentional injury in infants (0–1 years. The aim of this study is to investigate correlates of unsupervised bathing. This cross-sectional study included 1,410 parents with an infant. Parents completed a questionnaire regarding supervision during bathing, socio-demographic factors, and Protection Motivation Theory-constructs. To determine correlates of parents who leave their infant unsupervised, logistic regression analyses were performed. Of the parents, 6.2% left their child unsupervised in the bathtub. Parents with older children (OR 1.24; 95%CI 1.00–1.54 were more likely to leave their child unsupervised in the bathtub. First-time parents (OR 0.59; 95%CI 0.36–0.97 and non-Western migrant fathers (OR 0.18; 95%CI 0.05–0.63 were less likely to leave their child unsupervised in the bathtub. Furthermore, parents who perceived higher self-efficacy (OR 0.57; 95%CI 0.47–0.69, higher response efficacy (OR 0.34; 95%CI 0.24–0.48, and higher severity (OR 0.74; 95%CI 0.58–0.93 were less likely to leave their child unsupervised. Since young children are at great risk of drowning if supervision is absent, effective strategies for drowning prevention should be developed and evaluated. In the meantime, health care professionals should inform parents with regard to the importance of supervision during bathing.

  17. Cluster headache

    Science.gov (United States)

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...

  18. Mathematical classification and clustering

    CERN Document Server

    Mirkin, Boris

    1996-01-01

    I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished Russian scholar in the areas of data analysis and decision making methodologies. The monograph is devoted entirely to clustering, a discipline dispersed through many theoretical and application areas, from mathematical statistics and combina­ torial optimization to biology, sociology and organizational structures. It compiles an immense amount of research done to date, including many original Russian de­ velopments never presented to the international community before (for instance, cluster-by-cluster versions of the K-Means method in Chapter 4 or uniform par­ titioning in Chapter 5). The author's approach, approximation clustering, allows him both to systematize a great part of the discipline and to develop many in­ novative methods in the framework of optimization problems. The optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in ...

  19. Projection-based curve clustering

    International Nuclear Information System (INIS)

    Auder, Benjamin; Fischer, Aurelie

    2012-01-01

    This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat a l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the 'true' ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem. (authors)

  20. Unsupervised classification of lidar-based vegetation structure metrics at Jean Lafitte National Historical Park and Preserve

    Science.gov (United States)

    Kranenburg, Christine J.; Palaseanu-Lovejoy, Monica; Nayegandhi, Amar; Brock, John; Woodman, Robert

    2012-01-01

    Traditional vegetation maps capture the horizontal distribution of various vegetation properties, for example, type, species and age/senescence, across a landscape. Ecologists have long known, however, that many important forest properties, for example, interior microclimate, carbon capacity, biomass and habitat suitability, are also dependent on the vertical arrangement of branches and leaves within tree canopies. The objective of this study was to use a digital elevation model (DEM) along with tree canopy-structure metrics derived from a lidar survey conducted using the Experimental Advanced Airborne Research Lidar (EAARL) to capture a three-dimensional view of vegetation communities in the Barataria Preserve unit of Jean Lafitte National Historical Park and Preserve, Louisiana. The EAARL instrument is a raster-scanning, full waveform-resolving, small-footprint, green-wavelength (532-nanometer) lidar system designed to map coastal bathymetry, topography and vegetation structure simultaneously. An unsupervised clustering procedure was then applied to the 3-dimensional-based metrics and DEM to produce a vegetation map based on the vertical structure of the park's vegetation, which includes a flotant marsh, scrub-shrub wetland, bottomland hardwood forest, and baldcypress-tupelo swamp forest. This study was completed in collaboration with the National Park Service Inventory and Monitoring Program's Gulf Coast Network. The methods presented herein are intended to be used as part of a cost-effective monitoring tool to capture change in park resources.

  1. New Statistical Methodology for Determining Cancer Clusters

    Science.gov (United States)

    The development of an innovative statistical technique that shows that women living in a broad stretch of the metropolitan northeastern United States, which includes Long Island, are slightly more likely to die from breast cancer than women in other parts of the Northeast.

  2. Unsupervised sub-categorization for object detection: fInding cars from a driving vehicle

    NARCIS (Netherlands)

    Wijnhoven, R.G.J.; With, de P.H.N.

    2011-01-01

    We present a novel algorithm for unsupervised subcategorization of an object class, in the context of object detection. Dividing the detection problem into smaller subproblems simplifies the object vs. background classification. The algorithm uses an iterative split-and-merge procedure and uses both

  3. Evaluating unsupervised thesaurus-based labeling of audiovisual content in an archive production environment

    NARCIS (Netherlands)

    de Boer, V.; Ordelman, Roeland J.; Schuurman, Josefien

    2016-01-01

    In this paper we report on a two-stage evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results for given requirements with respect to archival quality, authority and service levels to external

  4. Best friends' interactions and substance use: The role of friend pressure and unsupervised co-deviancy.

    Science.gov (United States)

    Tsakpinoglou, Florence; Poulin, François

    2017-10-01

    Best friends exert a substantial influence on rising alcohol and marijuana use during adolescence. Two mechanisms occurring within friendship - friend pressure and unsupervised co-deviancy - may partially capture the way friends influence one another. The current study aims to: (1) examine the psychometric properties of a new instrument designed to assess pressure from a youth's best friend and unsupervised co-deviancy; (2) investigate the relative contribution of these processes to alcohol and marijuana use; and (3) determine whether gender moderates these associations. Data were collected through self-report questionnaires completed by 294 Canadian youths (62% female) across two time points (ages 15-16). Principal component analysis yielded a two-factor solution corresponding to friend pressure and unsupervised co-deviancy. Logistic regressions subsequently showed that unsupervised co-deviancy was predictive of an increase in marijuana use one year later. Neither process predicted an increase in alcohol use. Results did not differ as a function of gender. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  5. Evaluating Unsupervised Thesaurus-based Labeling of Audiovisual Content in an Archive Production Environment

    NARCIS (Netherlands)

    de Boer, Victor; Ordelman, Roeland J.F.; Schuurman, Josefien

    In this paper we report on a two-stage evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results for given requirements with respect to archival quality, authority and service levels to external

  6. Practice-Oriented Evaluation of Unsupervised Labeling of Audiovisual Content in an Archive Production Environment

    NARCIS (Netherlands)

    de Boer, Victor; Ordelman, Roeland J.F.; Schuurman, Josefien

    In this paper we report on an evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results given requirements with respect to archival quality, authority and service levels to external users. We

  7. A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

    Science.gov (United States)

    Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

    2015-01-01

    Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

  8. Hanging out with Which Friends? Friendship-Level Predictors of Unstructured and Unsupervised Socializing in Adolescence

    Science.gov (United States)

    Siennick, Sonja E.; Osgood, D. Wayne

    2012-01-01

    Companions are central to explanations of the risky nature of unstructured and unsupervised socializing, yet we know little about whom adolescents are with when hanging out. We examine predictors of how often friendship dyads hang out via multilevel analyses of longitudinal friendship-level data on over 5,000 middle schoolers. Adolescents hang out…

  9. A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images.

    Science.gov (United States)

    Gao, Han; Tang, Yunwei; Jing, Linhai; Li, Hui; Ding, Haifeng

    2017-10-24

    The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

  10. A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Han Gao

    2017-10-01

    Full Text Available The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA. Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

  11. Supervised and unsupervised condition monitoring of non-stationary acoustic emission signals

    DEFF Research Database (Denmark)

    Sigurdsson, Sigurdur; Pontoppidan, Niels Henrik; Larsen, Jan

    2005-01-01

    condition changes across load changes. In this paper we approach this load interpolation problem with supervised and unsupervised learning, i.e. model with normal and fault examples and normal examples only, respectively. We apply non-linear methods for the learning of engine condition changes. Both...

  12. PosQ: Unsupervised Fingerprinting and Visualization of GPS Positioning Quality

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Weckemann, Kay

    . This paper proposes PosQ, a system for unsupervised fingerprinting and visualization of GPS positioning quality. PosQ provides quality maps to position-based applications and visual overlays to users and managers to reveal the positioning quality in a local environment. The system reveals the quality both...

  13. A method for unsupervised change detection and automatic radiometric normalization in multispectral data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton John

    2011-01-01

    Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in North...... to carry out the analyses is available from the authors' websites....

  14. An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information

    Science.gov (United States)

    Snyder, Robin M.

    2015-01-01

    The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and…

  15. Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning

    NARCIS (Netherlands)

    Twellmann, T.; Meyer-Bäse, A.; Lange, O.; Foo, S.; Nattkemper, T.W.

    2008-01-01

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition

  16. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  17. Comparative analysis of clustering methods for gene expression time course data

    Directory of Open Access Journals (Sweden)

    Ivan G. Costa

    2004-01-01

    Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

  18. Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

    Science.gov (United States)

    Verhoeven, Thibault; Schmid, Konstantin; Müller, Klaus-Robert; Tangermann, Michael; Kindermans, Pieter-Jan

    2017-01-01

    Objective Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means. Method We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task. Results Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP. PMID:28407016

  19. Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints

    Directory of Open Access Journals (Sweden)

    Diego Peluffo-Ordóñez

    2017-08-01

    Full Text Available To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM. For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.

  20. Kernel method for clustering based on optimal target vector

    International Nuclear Information System (INIS)

    Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-01-01

    We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

  1. AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

    Science.gov (United States)

    Dasarathy, B. V.

    1976-01-01

    An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

  2. Cluster management.

    Science.gov (United States)

    Katz, R

    1992-11-01

    Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

  3. Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

    Science.gov (United States)

    2017-01-01

    In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as Nm, which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies. PMID:28841719

  4. Maximum Margin Clustering of Hyperspectral Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2013-09-01

    In recent decades, large margin methods such as Support Vector Machines (SVMs) are supposed to be the state-of-the-art of supervised learning methods for classification of hyperspectral data. However, the results of these algorithms mainly depend on the quality and quantity of available training data. To tackle down the problems associated with the training data, the researcher put effort into extending the capability of large margin algorithms for unsupervised learning. One of the recent proposed algorithms is Maximum Margin Clustering (MMC). The MMC is an unsupervised SVMs algorithm that simultaneously estimates both the labels and the hyperplane parameters. Nevertheless, the optimization of the MMC algorithm is a non-convex problem. Most of the existing MMC methods rely on the reformulating and the relaxing of the non-convex optimization problem as semi-definite programs (SDP), which are computationally very expensive and only can handle small data sets. Moreover, most of these algorithms are two-class classification, which cannot be used for classification of remotely sensed data. In this paper, a new MMC algorithm is used that solve the original non-convex problem using Alternative Optimization method. This algorithm is also extended for multi-class classification and its performance is evaluated. The results of the proposed algorithm show that the algorithm has acceptable results for hyperspectral data clustering.

  5. Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering

    International Nuclear Information System (INIS)

    Acton, P.D.; Pilowsky, L.S.; Kung, H.F.; Ell, P.J.

    1999-01-01

    The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D 2 /D 3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [ 123 I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r 2 >0.99, P 123 I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r 2 =0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. (orig.)

  6. Isotopic clusters

    International Nuclear Information System (INIS)

    Geraedts, J.M.P.

    1983-01-01

    Spectra of isotopically mixed clusters (dimers of SF 6 ) are calculated as well as transition frequencies. The result leads to speculations about the suitability of the laser-cluster fragmentation process for isotope separation. (Auth.)

  7. Cluster Headache

    Science.gov (United States)

    ... a role. Unlike migraine and tension headache, cluster headache generally isn't associated with triggers, such as foods, hormonal changes or stress. Once a cluster period begins, however, drinking alcohol ...

  8. Cluster Headache

    OpenAIRE

    Pearce, Iris

    1985-01-01

    Cluster headache is the most severe primary headache with recurrent pain attacks described as worse than giving birth. The aim of this paper was to make an overview of current knowledge on cluster headache with a focus on pathophysiology and treatment. This paper presents hypotheses of cluster headache pathophysiology, current treatment options and possible future therapy approaches. For years, the hypothalamus was regarded as the key structure in cluster headache, but is now thought to be pa...

  9. Categorias Cluster

    OpenAIRE

    Queiroz, Dayane Andrade

    2015-01-01

    Neste trabalho apresentamos as categorias cluster, que foram introduzidas por Aslak Bakke Buan, Robert Marsh, Markus Reineke, Idun Reiten e Gordana Todorov, com o objetivo de categoriíicar as algebras cluster criadas em 2002 por Sergey Fomin e Andrei Zelevinsky. Os autores acima, em [4], mostraram que existe uma estreita relação entre algebras cluster e categorias cluster para quivers cujo grafo subjacente é um diagrama de Dynkin. Para isto desenvolveram uma teoria tilting na estrutura triang...

  10. Genomic signal processing for DNA sequence clustering.

    Science.gov (United States)

    Mendizabal-Ruiz, Gerardo; Román-Godínez, Israel; Torres-Ramos, Sulema; Salido-Ruiz, Ricardo A; Vélez-Pérez, Hugo; Morales, J Alejandro

    2018-01-01

    Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

  11. Meaningful Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.

    2004-05-26

    We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.

  12. Unsupervised learning in neural networks with short range synapses

    Science.gov (United States)

    Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

    2013-01-01

    Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

  13. Software usage in unsupervised digital doorway computing environments in disadvantaged South African communities: Focusing on youthful users

    CSIR Research Space (South Africa)

    Gush, K

    2011-01-01

    Full Text Available Digital Doorways provide computing infrastructure in low-income communities in South Africa. The unsupervised DD terminals offer various software applications, from entertainment through educational resources to research material, encouraging...

  14. Cluster Matters

    DEFF Research Database (Denmark)

    Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha

    2018-01-01

    sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....

  15. Towards Statistical Unsupervised Online Learning for Music Listening with Hearing Devices

    DEFF Research Database (Denmark)

    Purwins, Hendrik; Marchini, Marco; Marxer, Richard

    of sounds into phonetic/instrument categories and learning of instrument event sequences is performed jointly using a Hierarchical Dirichlet Process Hidden Markov Model. Whereas machines often learn by processing a large data base and subsequently updating parameters of the algorithm, humans learn...... and their respective transition counts. We propose to use online learning for the co-evolution of both CI user and machine in (re-)learning musical language. [1] Marco Marchini and Hendrik Purwins. Unsupervised analysis and generation of audio percussion sequences. In International Symposium on Computer Music Modeling...... categories) as well as the temporal context horizon (e.g. storing up to 2-note sequences or up to 10-note sequences) is adaptable. The framework in [1] is based on two cognitively plausible principles: unsupervised learning and statistical learning. Opposed to supervised learning in primary school children...

  16. Large-Scale Unsupervised Hashing with Shared Structure Learning.

    Science.gov (United States)

    Liu, Xianglong; Mu, Yadong; Zhang, Danchen; Lang, Bo; Li, Xuelong

    2015-09-01

    Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.

  17. Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition

    OpenAIRE

    Huang, Furong; Anandkumar, Animashree

    2016-01-01

    Unsupervised text embeddings extraction is crucial for text understanding in machine learning. Word2Vec and its variants have received substantial success in mapping words with similar syntactic or semantic meaning to vectors close to each other. However, extracting context-aware word-sequence embedding remains a challenging task. Training over large corpus is difficult as labels are difficult to get. More importantly, it is challenging for pre-trained models to obtain word-...

  18. Modeling Language and Cognition with Deep Unsupervised Learning:A Tutorial Overview

    OpenAIRE

    Marco eZorzi; Marco eZorzi; Alberto eTestolin; Ivilin Peev Stoianov; Ivilin Peev Stoianov

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cog...

  19. Modeling language and cognition with deep unsupervised learning: a tutorial overview

    OpenAIRE

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cog...

  20. Evaluating unsupervised methods to size and classify suspended particles using digital in-line holography

    Science.gov (United States)

    Davies, Emlyn J.; Buscombe, Daniel D.; Graham, George W.; Nimmo-Smith, W. Alex M.

    2015-01-01

    Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing and classification of particles. These computational challenges are the subject of this paper, in which we draw upon data collected using a variety of holographic systems developed at Plymouth University, UK, from a significant range of particle types, sizes and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from over-segmentation of particles. A simple unsupervised particle classification system is developed, and is capable of successfully differentiating sand grains, bubbles and diatoms from within the surf-zone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations, and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments and oil droplets.

  1. Validating the use of Hospital Episode Statistics data and comparison of costing methodologies for economic evaluation: an end-of-life case study from the Cluster randomised triAl of PSA testing for Prostate cancer (CAP).

    Science.gov (United States)

    Thorn, Joanna C; Turner, Emma L; Hounsome, Luke; Walsh, Eleanor; Down, Liz; Verne, Julia; Donovan, Jenny L; Neal, David E; Hamdy, Freddie C; Martin, Richard M; Noble, Sian M

    2016-04-29

    To evaluate the accuracy of routine data for costing inpatient resource use in a large clinical trial and to investigate costing methodologies. Final-year inpatient cost profiles were derived using (1) data extracted from medical records mapped to the National Health Service (NHS) reference costs via service codes and (2) Hospital Episode Statistics (HES) data using NHS reference costs. Trust finance departments were consulted to obtain costs for comparison purposes. 7 UK secondary care centres. A subsample of 292 men identified as having died at least a year after being diagnosed with prostate cancer in Cluster randomised triAl of PSA testing for Prostate cancer (CAP), a long-running trial to evaluate the effectiveness and cost-effectiveness of prostate-specific antigen (PSA) testing. Both inpatient cost profiles showed a rise in costs in the months leading up to death, and were broadly similar. The difference in mean inpatient costs was £899, with HES data yielding ∼8% lower costs than medical record data (differences compatible with chance, p=0.3). Events were missing from both data sets. 11 men (3.8%) had events identified in HES that were all missing from medical record review, while 7 men (2.4%) had events identified in medical record review that were all missing from HES. The response from finance departments to requests for cost data was poor: only 3 of 7 departments returned adequate data sets within 6 months. Using HES routine data coupled with NHS reference costs resulted in mean annual inpatient costs that were very similar to those derived via medical record review; therefore, routinely available data can be used as the primary method of costing resource use in large clinical trials. Neither HES nor medical record review represent gold standards of data collection. Requesting cost data from finance departments is impractical for large clinical trials. ISRCTN92187251; Pre-results. Published by the BMJ Publishing Group Limited. For permission to use

  2. Validation of a free software for unsupervised assessment of abdominal fat in MRI.

    Science.gov (United States)

    Maddalo, Michele; Zorza, Ivan; Zubani, Stefano; Nocivelli, Giorgio; Calandra, Giulio; Soldini, Pierantonio; Mascaro, Lorella; Maroldi, Roberto

    2017-05-01

    To demonstrate the accuracy of an unsupervised (fully automated) software for fat segmentation in magnetic resonance imaging. The proposed software is a freeware solution developed in ImageJ that enables the quantification of metabolically different adipose tissues in large cohort studies. The lumbar part of the abdomen (19cm in craniocaudal direction, centered in L3) of eleven healthy volunteers (age range: 21-46years, BMI range: 21.7-31.6kg/m 2 ) was examined in a breath hold on expiration with a GE T1 Dixon sequence. Single-slice and volumetric data were considered for each subject. The results of the visceral and subcutaneous adipose tissue assessments obtained by the unsupervised software were compared to supervised segmentations of reference. The associated statistical analysis included Pearson correlations, Bland-Altman plots and volumetric differences (VD % ). Values calculated by the unsupervised software significantly correlated with corresponding supervised segmentations of reference for both subcutaneous adipose tissue - SAT (R=0.9996, psoftware is capable of segmenting the metabolically different adipose tissues with a high degree of accuracy. This free add-on software for ImageJ can easily have a widespread and enable large-scale population studies regarding the adipose tissue and its related diseases. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  3. Unsupervised categorization with individuals diagnosed as having moderate traumatic brain injury: Over-selective responding.

    Science.gov (United States)

    Edwards, Darren J; Wood, Rodger

    2016-01-01

    This study explored over-selectivity (executive dysfunction) using a standard unsupervised categorization task. Over-selectivity has been demonstrated using supervised categorization procedures (where training is given); however, little has been done in the way of unsupervised categorization (without training). A standard unsupervised categorization task was used to assess levels of over-selectivity in a traumatic brain injury (TBI) population. Individuals with TBI were selected from the Tertiary Traumatic Brain Injury Clinic at Swansea University and were asked to categorize two-dimensional items (pictures on cards), into groups that they felt were most intuitive, and without any learning (feedback from experimenter). This was compared against categories made by a control group for the same task. The findings of this study demonstrate that individuals with TBI had deficits for both easy and difficult categorization sets, as indicated by a larger amount of one-dimensional sorting compared to control participants. Deficits were significantly greater for the easy condition. The implications of these findings are discussed in the context of over-selectivity, and the processes that underlie this deficit. Also, the implications for using this procedure as a screening measure for over-selectivity in TBI are discussed.

  4. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

    Science.gov (United States)

    Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

    2017-01-01

    Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

  5. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Qi Huang

    2017-06-01

    Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

  6. Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction.

    Science.gov (United States)

    Nie, Feiping; Xu, Dong; Tsang, Ivor Wai-Hung; Zhang, Changshui

    2010-07-01

    We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F(0) = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F(0). Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.

  7. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

    Science.gov (United States)

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

    Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

  8. Identifying influential individuals on intensive care units: using cluster analysis to explore culture.

    Science.gov (United States)

    Fong, Allan; Clark, Lindsey; Cheng, Tianyi; Franklin, Ella; Fernandez, Nicole; Ratwani, Raj; Parker, Sarah Henrickson

    2017-07-01

    The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis. Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified. A social network analysis survey was conducted and an unsupervised cluster analysis was used. A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational. Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions. This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture. © 2017 John Wiley & Sons Ltd.

  9. Automated lesion detection on MRI scans using combined unsupervised and supervised methods

    International Nuclear Information System (INIS)

    Guo, Dazhou; Fridriksson, Julius; Fillmore, Paul; Rorden, Christopher; Yu, Hongkai; Zheng, Kang; Wang, Song

    2015-01-01

    Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a

  10. Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data.

    Science.gov (United States)

    Fiorini, Laura; Cavallo, Filippo; Dario, Paolo; Eavis, Alexandra; Caleb-Solly, Praminda

    2017-05-04

    The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people's homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users' behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a "blind" approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a "busyness" measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person's needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.

  11. Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

    Directory of Open Access Journals (Sweden)

    Laura Fiorini

    2017-05-01

    Full Text Available The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%. The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.

  12. Unsupervised motion-based object segmentation refined by color

    Science.gov (United States)

    Piek, Matthijs C.; Braspenning, Ralph; Varekamp, Chris

    2003-06-01

    for its ability to estimate motion vectors which closely resemble the true motion. BLOCK-BASED MOTION SEGMENTATION As mentioned above we start with a block-resolution segmentation based on motion vectors. The presented method is inspired by the well-known K-means segmentation method te{K-means}. Several other methods (e.g. te{kmeansc}) adapt K-means for connectedness by adding a weighted shape-error. This adds the additional difficulty of finding the correct weights for the shape-parameters. Also, these methods often bias one particular pre-defined shape. The presented method, which we call K-regions, encourages connectedness because only blocks at the edges of segments may be assigned to another segment. This constrains the segmentation method to such a degree that it allows the method to use least squares for the robust fitting of affine motion models for each segment. Contrary to te{parmkm}, the segmentation step still operates on vectors instead of model parameters. To make sure the segmentation is temporally consistent, the segmentation of the previous frame will be used as initialisation for every new frame. We also present a scheme which makes the algorithm independent of the initially chosen amount of segments. COLOUR-BASED INTRA-BLOCK SEGMENTATION The block resolution motion-based segmentation forms the starting point for the pixel resolution segmentation. The pixel resolution segmentation is obtained from the block resolution segmentation by reclassifying pixels only at the edges of clusters. We assume that an edge between two objects can be found in either one of two neighbouring blocks that belong to different clusters. This assumption allows us to do the pixel resolution segmentation on each pair of such neighbouring blocks separately. Because of the local nature of the segmentation, it largely avoids problems with heterogeneously coloured areas. Because no new segments are introduced in this step, it also does not suffer from oversegmentation problems

  13. Cluster evolution

    International Nuclear Information System (INIS)

    Schaeffer, R.

    1987-01-01

    The galaxy and cluster luminosity functions are constructed from a model of the mass distribution based on hierarchical clustering at an epoch where the matter distribution is non-linear. These luminosity functions are seen to reproduce the present distribution of objects as can be inferred from the observations. They can be used to deduce the redshift dependence of the cluster distribution and to extrapolate the observations towards the past. The predicted evolution of the cluster distribution is quite strong, although somewhat less rapid than predicted by the linear theory

  14. Unsupervised DInSAR processing chain for multi-scale displacement analysis

    Science.gov (United States)

    Casu, Francesco; Manunta, Michele

    2016-04-01

    Earth Observation techniques can be very helpful for the estimation of several sources of ground deformation due to their characteristics of large spatial coverage, high resolution and cost effectiveness. In this scenario, Differential Synthetic Aperture Radar Interferometry (DInSAR) is one of the most effective methodologies for its capability to generate spatially dense deformation maps at both global and local spatial scale, with centimeter to millimeter accuracy. DInSAR exploits the phase difference (interferogram) between SAR image pairs relevant to acquisitions gathered at different times, but with the same illumination geometry and from sufficiently close flight tracks, whose separation is typically referred to as baseline. Among several, the SBAS algorithm is one of the most used DInSAR approaches and it is aimed at generating displacement time series at a multi-scale level by exploiting a set of small baseline interferograms. SBAS, and generally DInSAR, has taken benefit from the large availability of spaceborne SAR data collected along years by several satellite systems, with particular regard to the European ERS and ENVISAT sensors, which have acquired SAR images worldwide during approximately 20 years. Moreover, since 2014 the new generation of Copernicus Sentinel satellites has started to acquire data with a short revisit time (12 days) and a global coverage policy, thus flooding the scientific EO community with an unprecedent amount of data. To efficiently manage such amount of data, proper processing facilities (as those coming from the emerging Cloud Computing technologies) have to be used, as well as novel algorithms aimed at their efficient exploitation have to be developed. In this work we present a set of results achieved by exploiting a recently proposed implementation of the SBAS algorithm, namely Parallel-SBAS (P-SBAS), which allows us to effectively process, in an unsupervised way and in a limited time frame, a huge number of SAR images

  15. A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring

    Science.gov (United States)

    Tibaduiza, D.-A.; Torres-Arredondo, M.-A.; Mujica, L. E.; Rodellar, J.; Fritzen, C.-P.

    2013-12-01

    This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly from the dynamic responses or from wavelet coefficients at different scales representing Time-frequency information. Different damages on the tested structures are simulated by adding masses at different positions. The data from the structure in different states (damaged or not) are then projected into the different principal component models by each actuator in order to obtain the input feature vectors for a SOM from the scores and the SPE measures. An aircraft fuselage from an Airbus A320 and a multi-layered carbon fiber reinforced plastic (CFRP) plate are used as examples to test the approaches. Results are presented, compared and discussed in order to determine their potential in structural health monitoring. These results showed that all the simulated damages were detectable and the selected features proved capable of separating all damage conditions from the undamaged state for both approaches.

  16. A new clustering algorithm for scanning electron microscope images

    Science.gov (United States)

    Yousef, Amr; Duraisamy, Prakash; Karim, Mohammad

    2016-04-01

    A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons. The electrons interact with the sample atoms, producing various signals that are collected by detectors. The gathered signals contain information about the sample's surface topography and composition. The electron beam is generally scanned in a raster scan pattern, and the beam's position is combined with the detected signal to produce an image. The most common configuration for an SEM produces a single value per pixel, with the results usually rendered as grayscale images. The captured images may be produced with insufficient brightness, anomalous contrast, jagged edges, and poor quality due to low signal-to-noise ratio, grained topography and poor surface details. The segmentation of the SEM images is a tackling problems in the presence of the previously mentioned distortions. In this paper, we are stressing on the clustering of these type of images. In that sense, we evaluate the performance of the well-known unsupervised clustering and classification techniques such as connectivity based clustering (hierarchical clustering), centroid-based clustering, distribution-based clustering and density-based clustering. Furthermore, we propose a new spatial fuzzy clustering technique that works efficiently on this type of images and compare its results against these regular techniques in terms of clustering validation metrics.

  17. Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches.

    Science.gov (United States)

    Butaciu, Sinziana; Senila, Marin; Sarbu, Costel; Ponta, Michaela; Tanaselia, Claudiu; Cadar, Oana; Roman, Marius; Radu, Emil; Sima, Mihaela; Frentiu, Tiberiu

    2017-04-01

    The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na + , K + , Ca 2+ , Mg 2+ , HCO 3 - , Cl - , F - , SO 4 2- , PO 4 3- , NO 3 - ), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F - species and the anthropogenic one for NO 3 - , SO 4 2- , PO 4 3- and K + and (ii) classification of groundwater based on content of arsenic species. The HCO 3 - type of local groundwater and alkaline pH (8.31-8.49) were found to be responsible for the enrichment of arsenic species and occurrence of F - but by different paths. The PO 4 3- -AsO 4 3- ion exchange, water-rock interaction (silicates hydrolysis and desorption from clay) were associated to arsenate enrichment in the oxidizing aquifer. Fuzzy Hierarchical Cross-Clustering was the strongest tool for the rapid simultaneous classification of groundwaters as a function of arsenic content and hydrogeochemical characteristics. The approach indicated the Na + -F - -pH cluster as marker for groundwater with naturally elevated As and highlighted which parameters need to be monitored. A chemical conceptual model illustrating the natural and anthropogenic paths and enrichment of As and co-occurring species in the local groundwater supported by mineralogical analysis of rocks was established. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

    Directory of Open Access Journals (Sweden)

    Xiangbing Zhou

    2018-04-01

    Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

  19. On methodology

    DEFF Research Database (Denmark)

    Cheesman, Robin; Faraone, Roque

    2002-01-01

    This is an English version of the methodology chapter in the authors' book "El caso Berríos: Estudio sobre información errónea, desinformación y manipulación de la opinión pública".......This is an English version of the methodology chapter in the authors' book "El caso Berríos: Estudio sobre información errónea, desinformación y manipulación de la opinión pública"....

  20. Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm

    OpenAIRE

    Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric

    2017-01-01

    This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was developed to implement a robotics application consisting in sorting and storing objects in an unsupervised way. The toy dataset used to validate such application consists of Lego bricks of different shapes and colors. The uncontrolled lighting conditions together with the use of RGB color features, respectively involve data...

  1. Cluster Validity Classification Approaches Based on Geometric Probability and Application in the Classification of Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    LI Jian-Wei

    2014-08-01

    Full Text Available On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability firstly, determine clusters and the gathering direction, then determine the center of clustering and the border of clusters. Through TM remote sensing image classification examples, compare with the supervision and unsupervised classification in ERDAS and the cluster analysis method based on geometric probability in two-dimensional square which is proposed in literature 2. Results show that the proposed method can significantly improve the classification accuracy.

  2. Macroeconomic Dimensions in the Clusterization Processes: Lithuanian Biomass Cluster Case

    Directory of Open Access Journals (Sweden)

    Navickas Valentinas

    2017-03-01

    Full Text Available The Future production systems’ increasing significance will impose work, which maintains not a competitive, but a collaboration basis, with concentrated resources and expertise, which can help to reach the general purpose. One form of collaboration among medium-size business organizations is work in clusters. Clusterization as a phenomenon has been known from quite a long time, but it offers simple benefits to researches at micro and medium levels. The clusterization process evaluation in macroeconomic dimensions has been comparatively little investigated. Thereby, in this article, the clusterization processes is analysed by concentrating our attention on macroeconomic factor researches. The authors analyse clusterization’s influence on country’s macroeconomic growth; they apply a structure research methodology for clusterization’s macroeconomic influence evaluation and propose that clusterization processes benefit macroeconomic analysis. The theoretical model of clusterization processes was validated by referring to a biomass cluster case. Because biomass cluster case is a new phenomenon, currently there are no other scientific approaches to them. The authors’ accomplished researches show that clusterization allows the achievement of a large positive slip in macroeconomics, which proves to lead to a high value added to creation, a faster country economic growth, and social situation amelioration.

  3. Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

    Science.gov (United States)

    Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

    Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object

  4. Individualized unsupervised exercise programs and chest physiotherapy in children with cystic fibrosis

    Directory of Open Access Journals (Sweden)

    Bogdan ALMĂJAN-GUȚĂ

    2013-12-01

    Full Text Available Traditionally, physiotherapy for cystic fibrosis focused mainly on airway clearance (clearing mucus from the lungs. This still makes up a large part of daily treatment, but the role of the physiotherapist in cystic fibrosis has expanded to include daily exercise, inhalation therapy, posture awareness and, for some, the management of urinary incontinence. The purpose of this study is to demonstrate the necessity and the efficiency of various methods of chest physiotherapy and individualized unsupervised exercise program, in the improvement of body composition and physical performance. This study included 12 children with cystic fibrosis, with ages between 8-13 years. Each subject was evaluated in terms of body composition, effort capacity and lower body muscular performance, at the beginning of the study and after 12 months.The intervention consisted in classic respiratory clearance and physiotherapy techniques (5 times a week and an individualized unsupervised exercise program (3 times a week. After 12 months we noticed a significant improvement of the measured parameters: body weight increased from 32.25±5.5 to 33.53±5.4 kg (p <0.001, skeletal muscle mass increased from a mean of 16.04±4.1 to 17.01±4.2 (p<0.001, the fitness score, increased from a mean of 71±3.8 points to73±3.8, (p<0.001 and power and force also registered positive evolutions (from 19.3±2.68 to 21.65±2.4 W/kg and respectively 19.68±2.689 to 20.81±2.98 N/kg.The association between physiotherapy procedures and an individualized (after a proper clinical assessment unsupervised exercise program, proved to be an effective, relatively simple and accessible (regardless of social class intervention.

  5. Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm.

    Science.gov (United States)

    Kamali, Tahereh; Stashuk, Daniel

    2016-10-01

    Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity. Copyright

  6. Clustering Dycom

    KAUST Repository

    Minku, Leandro L.

    2017-10-06

    Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

  7. Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

    Science.gov (United States)

    Liu, Wuying; Wang, Lin

    2018-03-01

    The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

  8. Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

    Science.gov (United States)

    Chen, Jinying; Yu, Hong

    2017-04-01

    Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, EHR notes contain abundant medical jargon that can be difficult for patients to comprehend. One way to help patients is to reduce information overload and help them focus on medical terms that matter most to them. Targeted education can then be developed to improve patient EHR comprehension and the quality of care. The aim of this work was to develop FIT (Finding Important Terms for patients), an unsupervised natural language processing (NLP) system that ranks medical terms in EHR notes based on their importance to patients. We built FIT on a new unsupervised ensemble ranking model derived from the biased random walk algorithm to combine heterogeneous information resources for ranking candidate terms from each EHR note. Specifically, FIT integrates four single views (rankers) for term importance: patient use of medical concepts, document-level term salience, word co-occurrence based term relatedness, and topic coherence. It also incorporates partial information of term importance as conveyed by terms' unfamiliarity levels and semantic types. We evaluated FIT on 90 expert-annotated EHR notes and used the four single-view rankers as baselines. In addition, we implemented three benchmark unsupervised ensemble ranking methods as strong baselines. FIT achieved 0.885 AUC-ROC for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FIT for identifying important terms from EHR notes was 0.813 AUC-ROC. Both performance scores significantly exceeded the corresponding scores from the four single rankers (P<0.001). FIT also outperformed the three ensemble rankers for most metrics. Its performance is relatively insensitive to its parameter. FIT can automatically identify EHR terms important to patients. It may help develop future interventions

  9. Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

    Energy Technology Data Exchange (ETDEWEB)

    Pinar, Ali [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Wake Forest Univ., Winston-Salem, MA (United States); Ballard, Grey [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mahoney, Michael [Univ. of California, Berkeley, CA (United States)

    2018-01-01

    Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

  10. Clustering analysis

    International Nuclear Information System (INIS)

    Romli

    1997-01-01

    Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

  11. Cluster analysis

    CERN Document Server

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  12. Cluster editing

    DEFF Research Database (Denmark)

    Böcker, S.; Baumbach, Jan

    2013-01-01

    . The problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications......The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side...

  13. An unsupervised meta-graph clustering based prototype-specific feature quantification for human re-identification in video surveillance

    Directory of Open Access Journals (Sweden)

    Aparajita Nanda

    2017-06-01

    Full Text Available Human re-identification is an emerging research area in the field of visual surveillance. It refers to the task of associating the images of the persons captured by one camera (probe set with the images captured by another camera (gallery set at different locations in different time instances. The performance of these systems are often challenged by some factors—variation in articulated human pose and clothing, frequent occlusion with various objects, change in light illumination, and the cluttered background are to name a few. Besides, the ambiguity in recognition increases between individuals with similar appearance. In this paper, we present a novel framework for human re-identification that finds the correspondence image pair across non-overlapping camera views in the presence of the above challenging scenarios. The proposed framework handles the visual ambiguity having similar appearance by first segmenting the gallery instances into disjoint prototypes (groups, where each prototype represents the images with high commonality. Then, a weighing scheme is formulated that quantifies the selective and distinct information about the features concerning the level of contribution against each prototype. Finally, the prototype specific weights are utilized in the similarity measure and fused with the existing generic weighing to facilitates improvement in the re-identification. Exhaustive simulation on three benchmark datasets alongside the CMC (Cumulative Matching Characteristics plot enumerate the efficacy of our proposed framework over the counterparts.

  14. Clustering analysis of malware behavior using Self Organizing Map

    DEFF Research Database (Denmark)

    Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...

  15. Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering.

    Science.gov (United States)

    He, Zhaoshui; Xie, Shengli; Zdunek, Rafal; Zhou, Guoxu; Cichocki, Andrzej

    2011-12-01

    Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.

  16. Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

    Science.gov (United States)

    Ahmadzadeh, Ezat; Jaferzadeh, Keyvan; Lee, Jieun; Moon, Inkyu

    2017-07-01

    We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.

  17. Occupational Clusters.

    Science.gov (United States)

    Pottawattamie County School System, Council Bluffs, IA.

    The 15 occupational clusters (transportation, fine arts and humanities, communications and media, personal service occupations, construction, hospitality and recreation, health occupations, marine science occupations, consumer and homemaking-related occupations, agribusiness and natural resources, environment, public service, business and office…

  18. Fuzzy Clustering

    DEFF Research Database (Denmark)

    Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan

    2000-01-01

    A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...

  19. Cluster generator

    Science.gov (United States)

    Donchev, Todor I [Urbana, IL; Petrov, Ivan G [Champaign, IL

    2011-05-31

    Described herein is an apparatus and a method for producing atom clusters based on a gas discharge within a hollow cathode. The hollow cathode includes one or more walls. The one or more walls define a sputtering chamber within the hollow cathode and include a material to be sputtered. A hollow anode is positioned at an end of the sputtering chamber, and atom clusters are formed when a gas discharge is generated between the hollow anode and the hollow cathode.

  20. Cluster Bulleticity

    OpenAIRE

    Massey, Richard; Kitching, Thomas; Nagai, Daisuke

    2010-01-01

    The unique properties of dark matter are revealed during collisions between clusters of galaxies, such as the bullet cluster (1E 0657−56) and baby bullet (MACS J0025−12). These systems provide evidence for an additional, invisible mass in the separation between the distributions of their total mass, measured via gravitational lensing, and their ordinary ‘baryonic’ matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by their rarity. C...

  1. Cluster headache

    OpenAIRE

    Leroux, Elizabeth; Ducros, Anne

    2008-01-01

    Abstract Cluster headache (CH) is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes) of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye). It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name) in bouts that can occur ...

  2. Six weeks of unsupervised Nintendo Wii Fit gaming is effective at improving balance in independent older adults.

    Science.gov (United States)

    Nicholson, Vaughan Patrick; McKean, Mark; Lowe, John; Fawcett, Christine; Burkett, Brendan

    2015-01-01

    To determine the effectiveness of unsupervised Nintendo Wii Fit balance training in older adults. Forty-one older adults were recruited from local retirement villages and educational settings to participate in a six-week two-group repeated measures study. The Wii group (n = 19, 75 ± 6 years) undertook 30 min of unsupervised Wii balance gaming three times per week in their retirement village while the comparison group (n = 22, 74 ± 5 years) continued with their usual exercise program. Participants' balance abilities were assessed pre- and postintervention. The Wii Fit group demonstrated significant improvements (P balance, lateral reach (left and right), and gait speed compared with the comparison group. Reported levels of enjoyment following game play increased during the study. Six weeks of unsupervised Wii balance training is an effective modality for improving balance in independent older adults.

  3. Unsupervised progressive elastic band exercises for frail geriatric inpatients objectively monitored by new exercise-integrated technology

    DEFF Research Database (Denmark)

    Rathleff, Camilla Rams; Bandholm, T.; Spaich, Erika Geraldina

    2017-01-01

    the amount of supervised training, and unsupervised training could possibly supplement supervised training thereby increasing the total exercise dose during admission. A new valid and reliable technology, the BandCizer, objectively measures the exact training dosage performed. The purpose was to investigate...... feasibility and acceptability of an unsupervised progressive strength training intervention monitored by BandCizer for frail geriatric inpatients. Methods: This feasibility trial included 15 frail inpatients at a geriatric ward. At hospitalization, the patients were prescribed two elastic band exercises...... of 2-min pauses and a time-under-tension of 8 s. The feasibility criterion for the unsupervised progressive exercises was that 33% of the recommended number of sets would be performed by at least 30% of patients. In addition, patients and staff were interviewed about their experiences...

  4. Methodological guidelines

    International Nuclear Information System (INIS)

    Halsnaes, K.; Callaway, J.M.; Meyer, H.J.

    1999-01-01

    The guideline document establishes a general overview of the main components of climate change mitigation assessment. This includes an outline of key economic concepts, scenario structure, common assumptions, modelling tools and country study assumptions. The guidelines are supported by Handbook Reports that contain more detailed specifications of calculation standards, input assumptions and available tools. The major objectives of the project have been provided a methodology, an implementing framework and a reporting system which countries can follow in meeting their future reporting obligations under the FCCC and for GEF enabling activities. The project builds upon the methodology development and application in the UNEP National Abatement Coasting Studies (UNEP, 1994a). The various elements provide countries with a road map for conducting climate change mitigation studies and submitting national reports as required by the FCCC. (au) 121 refs

  5. Methodological guidelines

    Energy Technology Data Exchange (ETDEWEB)

    Halsnaes, K.; Callaway, J.M.; Meyer, H.J.

    1999-04-01

    The guideline document establishes a general overview of the main components of climate change mitigation assessment. This includes an outline of key economic concepts, scenario structure, common assumptions, modelling tools and country study assumptions. The guidelines are supported by Handbook Reports that contain more detailed specifications of calculation standards, input assumptions and available tools. The major objectives of the project have been provided a methodology, an implementing framework and a reporting system which countries can follow in meeting their future reporting obligations under the FCCC and for GEF enabling activities. The project builds upon the methodology development and application in the UNEP National Abatement Coasting Studies (UNEP, 1994a). The various elements provide countries with a road map for conducting climate change mitigation studies and submitting national reports as required by the FCCC. (au) 121 refs.

  6. Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

    Science.gov (United States)

    McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

    2016-12-01

    Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

  7. Combining cluster number counts and galaxy clustering

    Energy Technology Data Exchange (ETDEWEB)

    Lacasa, Fabien; Rosenfeld, Rogerio, E-mail: fabien@ift.unesp.br, E-mail: rosenfel@ift.unesp.br [ICTP South American Institute for Fundamental Research, Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo (Brazil)

    2016-08-01

    The abundance of clusters and the clustering of galaxies are two of the important cosmological probes for current and future large scale surveys of galaxies, such as the Dark Energy Survey. In order to combine them one has to account for the fact that they are not independent quantities, since they probe the same density field. It is important to develop a good understanding of their correlation in order to extract parameter constraints. We present a detailed modelling of the joint covariance matrix between cluster number counts and the galaxy angular power spectrum. We employ the framework of the halo model complemented by a Halo Occupation Distribution model (HOD). We demonstrate the importance of accounting for non-Gaussianity to produce accurate covariance predictions. Indeed, we show that the non-Gaussian covariance becomes dominant at small scales, low redshifts or high cluster masses. We discuss in particular the case of the super-sample covariance (SSC), including the effects of galaxy shot-noise, halo second order bias and non-local bias. We demonstrate that the SSC obeys mathematical inequalities and positivity. Using the joint covariance matrix and a Fisher matrix methodology, we examine the prospects of combining these two probes to constrain cosmological and HOD parameters. We find that the combination indeed results in noticeably better constraints, with improvements of order 20% on cosmological parameters compared to the best single probe, and even greater improvement on HOD parameters, with reduction of error bars by a factor 1.4-4.8. This happens in particular because the cross-covariance introduces a synergy between the probes on small scales. We conclude that accounting for non-Gaussian effects is required for the joint analysis of these observables in galaxy surveys.

  8. Current trends in Bayesian methodology with applications

    CERN Document Server

    Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia

    2015-01-01

    Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on

  9. Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

    Directory of Open Access Journals (Sweden)

    Suzuki Motoyuki

    2009-01-01

    Full Text Available Abstract We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the "query relevance." Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29% was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%.

  10. Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

    Directory of Open Access Journals (Sweden)

    Akinori Ito

    2009-01-01

    Full Text Available We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the “query relevance.” Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29% was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%.

  11. Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging

    Energy Technology Data Exchange (ETDEWEB)

    Dunlavy, Daniel; Peter A. Chew

    2016-05-01

    This reports describes extensions of DEDICOM (DEcomposition into DIrectional COMponents) data models [3] that incorporate bound and linear constraints. The main purpose of these extensions is to investigate the use of improved data models for unsupervised part-of-speech tagging, as described by Chew et al. [2]. In that work, a single domain, two-way DEDICOM model was computed on a matrix of bigram fre- quencies of tokens in a corpus and used to identify parts-of-speech as an unsupervised approach to that problem. An open problem identi ed in that work was the com- putation of a DEDICOM model that more closely resembled the matrices used in a Hidden Markov Model (HMM), speci cally through post-processing of the DEDICOM factor matrices. The work reported here consists of the description of several models that aim to provide a direct solution to that problem and a way to t those models. The approach taken here is to incorporate the model requirements as bound and lin- ear constrains into the DEDICOM model directly and solve the data tting problem as a constrained optimization problem. This is in contrast to the typical approaches in the literature, where the DEDICOM model is t using unconstrained optimization approaches, and model requirements are satis ed as a post-processing step.

  12. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Jun He

    2017-07-01

    Full Text Available Artificial intelligence (AI techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN and support vector machine (SVM. The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  13. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Directory of Open Access Journals (Sweden)

    Yoonsik Shim

    2016-10-01

    Full Text Available We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP. The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  14. Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

    Science.gov (United States)

    Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

    2017-11-01

    The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

  15. Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

    Science.gov (United States)

    Schouten, Kim; van der Weijde, Onne; Frasincar, Flavius; Dekker, Rommert

    2018-04-01

    Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an -score of 67%. The second method is a supervised variant that outperforms existing methods with an -score of 84%.

  16. An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning

    Directory of Open Access Journals (Sweden)

    Xiangchun Yu

    2018-01-01

    Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.

  17. Performance of some supervised and unsupervised multivariate techniques for grouping authentic and unauthentic Viagra and Cialis

    Directory of Open Access Journals (Sweden)

    Michel J. Anzanello

    2014-09-01

    Full Text Available A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, the performance of several methods belonging to two different classes of multivariate techniques–supervised and unsupervised techniques–were compared. The supervised techniques (ST are the k-Nearest Neighbor (KNN, Support Vector Machine (SVM, Probabilistic Neural Networks (PNN and Linear Discriminant Analysis (LDA; the unsupervised techniques are the k-Means CA and the Fuzzy C-Means (FCM. The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.

  18. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Science.gov (United States)

    Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

    2016-10-01

    We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  19. Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

    Directory of Open Access Journals (Sweden)

    Yue Li

    2017-03-01

    Full Text Available This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series. The main challenges here are: (1 definition of the symbol assignment for the time series; (2 identification of the partitioning segment locations in the signal space of time series; and (3 construction of probabilistic finite-state automata (PFSA from the symbol strings that contain temporal patterns. The reported work addresses these challenges by maximizing the mutual information measures between symbol strings and PFSA states. The proposed symbolization method has been validated by numerical simulation as well as by experimentation in a laboratory environment. Performance of the proposed algorithm has been compared to that of two commonly used algorithms of time series partitioning.

  20. Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

    Science.gov (United States)

    Niegowski, Maciej; Zivanovic, Miroslav

    2016-03-01

    We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  1. Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Jui-Yuan Su

    2015-04-01

    Full Text Available In this paper we present a novel unsupervised approach to detecting and segmenting objects as well as their constituent symmetric parts in an image. Traditional unsupervised image segmentation is limited by two obvious deficiencies: the object detection accuracy degrades with the misaligned boundaries between the segmented regions and the target, and pre-learned models are required to group regions into meaningful objects. To tackle these difficulties, the proposed approach aims at incorporating the pair-wise detection of symmetric patches to achieve the goal of segmenting images into symmetric parts. The skeletons of these symmetric parts then provide estimates of the bounding boxes to locate the target objects. Finally, for each detected object, the graphcut-based segmentation algorithm is applied to find its contour. The proposed approach has significant advantages: no a priori object models are used, and multiple objects are detected. To verify the effectiveness of the approach based on the cues that a face part contains an oval shape and skin colors, human objects are extracted from among the detected objects. The detected human objects and their parts are finally tracked across video frames to capture the object part movements for learning the human activity models from video clips. Experimental results show that the proposed method gives good performance on publicly available datasets.

  2. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

    Science.gov (United States)

    He, Jun; Yang, Shixi; Gan, Chunbiao

    2017-07-04

    Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  3. Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

    Science.gov (United States)

    Azcorra, A; Chiroque, L F; Cuevas, R; Fernández Anta, A; Laniado, H; Lillo, R E; Romo, J; Sguera, C

    2018-05-03

    Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

  4. A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection.

    Science.gov (United States)

    Ceccarelli, Michele; d'Acierno, Antonio; Facchiano, Angelo

    2009-10-15

    Mass spectrometry spectra, widely used in proteomics studies as a screening tool for protein profiling and to detect discriminatory signals, are high dimensional data. A large number of local maxima (a.k.a. peaks) have to be analyzed as part of computational pipelines aimed at the realization of efficient predictive and screening protocols. With this kind of data dimensions and samples size the risk of over-fitting and selection bias is pervasive. Therefore the development of bio-informatics methods based on unsupervised feature extraction can lead to general tools which can be applied to several fields of predictive proteomics. We propose a method for feature selection and extraction grounded on the theory of multi-scale spaces for high resolution spectra derived from analysis of serum. Then we use support vector machines for classification. In particular we use a database containing 216 samples spectra divided in 115 cancer and 91 control samples. The overall accuracy averaged over a large cross validation study is 98.18. The area under the ROC curve of the best selected model is 0.9962. We improved previous known results on the problem on the same data, with the advantage that the proposed method has an unsupervised feature selection phase. All the developed code, as MATLAB scripts, can be downloaded from http://medeaserver.isa.cnr.it/dacierno/spectracode.htm.

  5. MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

    Directory of Open Access Journals (Sweden)

    Ashlock Daniel

    2009-08-01

    Full Text Available Abstract Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

  6. MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

    Science.gov (United States)

    Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

    2009-08-22

    Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

  7. Changing cluster composition in cluster randomised controlled trials: design and analysis considerations

    Science.gov (United States)

    2014-01-01

    Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations

  8. CSR in Industrial Clusters

    DEFF Research Database (Denmark)

    Lund-Thomsen, Peter; Pillay, Renginee G.

    2012-01-01

    Purpose – The paper seeks to review the literature on CSR in industrial clusters in developing countries, identifying the main strengths, weaknesses, and gaps in this literature, pointing to future research directions and policy implications in the area of CSR and industrial cluster development....... Design/methodology/approach – A literature review is conducted of both academic and policy-oriented writings that contain the keywords “industrial clusters” and “developing countries” in combination with one or more of the following terms: corporate social responsibility, environmental management, labor...... standards, child labor, climate change, social upgrading, and environmental upgrading. The authors examine the key themes in this literature, identify the main gaps, and point to areas where future work in this area could usefully be undertaken. Feedback has been sought from some of the leading authors...

  9. Hierarchical clustering of HPV genotype patterns in the ASCUS-LSIL triage study

    Science.gov (United States)

    Wentzensen, Nicolas; Wilson, Lauren E.; Wheeler, Cosette M.; Carreon, Joseph D.; Gravitt, Patti E.; Schiffman, Mark; Castle, Philip E.

    2010-01-01

    Anogenital cancers are associated with about 13 carcinogenic HPV types in a broader group that cause cervical intraepithelial neoplasia (CIN). Multiple concurrent cervical HPV infections are common which complicate the attribution of HPV types to different grades of CIN. Here we report the analysis of HPV genotype patterns in the ASCUS-LSIL triage study using unsupervised hierarchical clustering. Women who underwent colposcopy at baseline (n = 2780) were grouped into 20 disease categories based on histology and cytology. Disease groups and HPV genotypes were clustered using complete linkage. Risk of 2-year cumulative CIN3+, viral load, colposcopic impression, and age were compared between disease groups and major clusters. Hierarchical clustering yielded four major disease clusters: Cluster 1 included all CIN3 histology with abnormal cytology; Cluster 2 included CIN3 histology with normal cytology and combinations with either CIN2 or high-grade squamous intraepithelial lesion (HSIL) cytology; Cluster 3 included older women with normal or low grade histology/cytology and low viral load; Cluster 4 included younger women with low grade histology/cytology, multiple infections, and the highest viral load. Three major groups of HPV genotypes were identified: Group 1 included only HPV16; Group 2 included nine carcinogenic types plus non-carcinogenic HPV53 and HPV66; and Group 3 included non-carcinogenic types plus carcinogenic HPV33 and HPV45. Clustering results suggested that colposcopy missed a prevalent precancer in many women with no biopsy/normal histology and HSIL. This result was confirmed by an elevated 2-year risk of CIN3+ in these groups. Our novel approach to study multiple genotype infections in cervical disease using unsupervised hierarchical clustering can address complex genotype distributions on a population level. PMID:20959485

  10. MIRD methodology

    International Nuclear Information System (INIS)

    Rojo, Ana M.; Gomez Parada, Ines

    2004-01-01

    The MIRD (Medical Internal Radiation Dose) system was established by the Society of Nuclear Medicine of USA in 1960 to assist the medical community in the estimation of the dose in organs and tissues due to the incorporation of radioactive materials. Since then, 'MIRD Dose Estimate Report' (from the 1 to 12) and 'Pamphlets', of great utility for the dose calculations, were published. The MIRD system was planned essentially for the calculation of doses received by the patients during nuclear medicine diagnostic procedures. The MIRD methodology for the absorbed doses calculations in different tissues is explained

  11. PSA methodology

    Energy Technology Data Exchange (ETDEWEB)

    Magne, L

    1997-12-31

    The purpose of this text is first to ask a certain number of questions on the methods related to PSAs. Notably we will explore the positioning of the French methodological approach - as applied in the EPS 1300{sup 1} and EPS 900{sup 2} PSAs - compared to other approaches (Part One). This reflection leads to more general reflection: what contents, for what PSA? This is why, in Part Two, we will try to offer a framework for definition of the criteria a PSA should satisfy to meet the clearly identified needs. Finally, Part Three will quickly summarize the questions approached in the first two parts, as an introduction to the debate. 15 refs.

  12. PSA methodology

    International Nuclear Information System (INIS)

    Magne, L.

    1996-01-01

    The purpose of this text is first to ask a certain number of questions on the methods related to PSAs. Notably we will explore the positioning of the French methodological approach - as applied in the EPS 1300 1 and EPS 900 2 PSAs - compared to other approaches (Part One). This reflection leads to more general reflection: what contents, for what PSA? This is why, in Part Two, we will try to offer a framework for definition of the criteria a PSA should satisfy to meet the clearly identified needs. Finally, Part Three will quickly summarize the questions approached in the first two parts, as an introduction to the debate. 15 refs

  13. Time series clustering in large data sets

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2011-01-01

    Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

  14. Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

    Science.gov (United States)

    Yau, Christopher; Holmes, Chris

    2011-07-01

    We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.

  15. Clustering performance comparison using K-means and expectation maximization algorithms.

    Science.gov (United States)

    Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

    2014-11-14

    Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

  16. Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

    Science.gov (United States)

    Iyigun, Cem; Ben-Israel, Adi

    Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

  17. Canonical PSO Based K-Means Clustering Approach for Real Datasets.

    Science.gov (United States)

    Dey, Lopamudra; Chakraborty, Sanjay

    2014-01-01

    "Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

  18. Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

    Science.gov (United States)

    Beltrame, Thomas; Amelard, Robert; Wong, Alexander; Hughson, Richard L

    2018-02-01

    Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o 2 ) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o 2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o 2 data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC HIP ), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o 2 . The aerobic system analysis was based on the frequency-domain analysis of ACC HIP and predicted V̇o 2 data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o 2 data during μADL correlated with the temporal characteristics of measured V̇o 2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable

  19. The delicate balance between parental protection, unsupervised wandering, and adolescents' autonomy and its relation with antisocial behavior : The TRAILS study

    NARCIS (Netherlands)

    Sentse, M.; Dijkstra, J.K.; Lindenberg, S.; Ormel, J.; Veenstra, R.

    In a large sample of early adolescents (T2: N = 1023; M age = 13.51; 55.5% girls), the impact of parental protection and unsupervised wandering on adolescents' antisocial behavior 2.5 years later was tested in this TRAILS study; gender and parental knowledge were controlled for. In addition, the

  20. The Delicate Balance between Parental Protection, Unsupervised Wandering, and Adolescents' Autonomy and Its Relation with Antisocial Behavior: The TRAILS Study

    Science.gov (United States)

    Sentse, Miranda; Dijkstra, Jan Kornelis; Lindenberg, Siegwart; Ormel, Johan; Veenstra, Rene

    2010-01-01

    In a large sample of early adolescents (T2: N = 1023; M age = 13.51; 55.5% girls), the impact of parental protection and unsupervised wandering on adolescents' antisocial behavior 2.5 years later was tested in this TRAILS study; gender and parental knowledge were controlled for. In addition, the level of biological maturation and having antisocial…

  1. Clustering Dycom

    KAUST Repository

    Minku, Leandro L.; Hou, Siqing

    2017-01-01

    baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number

  2. Unsupervised Learning of Structural Representation of Percussive Audio Using a Hierarchical Dirichlet Process Hidden Markov Model

    DEFF Research Database (Denmark)

    Antich, Jose Luis Diez; Paterna, Mattia; Marxer, Richard

    2016-01-01

    to jointly estimate the optimal number of sound clusters, to cluster the blocks, and to estimate the transition probabilities between clusters. The result is a segmentation of the input into a sequence of symbols (typically corresponding to hits of hi-hat, snare, bass, cymbal, etc.) that can be evaluated...

  3. Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing

    Science.gov (United States)

    Jansen, Peter A.; Watter, Scott

    2012-03-01

    Connectionist language modelling typically has difficulty with syntactic systematicity, or the ability to generalise language learning to untrained sentences. This work develops an unsupervised connectionist model of infant grammar learning. Following the semantic boostrapping hypothesis, the network distils word category using a developmentally plausible infant-scale database of grounded sensorimotor conceptual representations, as well as a biologically plausible semantic co-occurrence activation function. The network then uses this knowledge to acquire an early benchmark clausal grammar using correlational learning, and further acquires separate conceptual and grammatical category representations. The network displays strongly systematic behaviour indicative of the general acquisition of the combinatorial systematicity present in the grounded infant-scale language stream, outperforms previous contemporary models that contain primarily noun and verb word categories, and successfully generalises broadly to novel untrained sensorimotor grounded sentences composed of unfamiliar nouns and verbs. Limitations as well as implications to later grammar learning are discussed.

  4. An Unsupervised Approach to Activity Recognition and Segmentation based on Object-Use Fingerprints

    DEFF Research Database (Denmark)

    Gu, Tao; Chen, Shaxun; Tao, Xianping

    2010-01-01

    Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised...... machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. We show how to build our activity models...... a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our...

  5. Hanging Out with Which Friends? Friendship-Level Predictors of Unstructured and Unsupervised Socializing in Adolescence.

    Science.gov (United States)

    Siennick, Sonja E; Osgood, D Wayne

    2012-12-01

    Companions are central to explanations of the risky nature of unstructured and unsupervised socializing, yet we know little about whom adolescents are with when hanging out. We examine predictors of how often friendship dyads hang out via multilevel analyses of longitudinal friendship-level data on over 5,000 middle schoolers. Adolescents hang out most with their most available friends and their most generally similar friends, not with their most at-risk or similarly at-risk friends. These findings vary little by gender and wave. Together, the findings suggest that the risks of hanging out stem from the nature of hanging out as an activity, not the nature of adolescents' companions, and that hanging out is a context for friends' mutual reinforcement of pre-existing characteristics.

  6. Modeling language and cognition with deep unsupervised learning: a tutorial overview.

    Science.gov (United States)

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

  7. Modeling Language and Cognition with Deep Unsupervised Learning:A Tutorial Overview

    Directory of Open Access Journals (Sweden)

    Marco eZorzi

    2013-08-01

    Full Text Available Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981 is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

  8. Technique Based on Image Pyramid and Bayes Rule for Noise Reduction in Unsupervised Change Detection

    Institute of Scientific and Technical Information of China (English)

    LI Zhi-qiang; HUO hong; FANG Tao; ZHU Ju-lian; GE Wei-li

    2009-01-01

    In this paper, a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed. By using Gaussian pyramid to process two multitemporal images respectively, two image pyramids are constructed. The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids. By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them, a map being similar to the difference image is obtained. The difference image is generated by point-by-point subtraction between the two multitemporal images directly. At last, the Bayes rule is used to distinguish the changed pixels. Both synthetic and real data sets are used to evaluate the performance of the proposed technique. Experimental results show that the map from the proposed technique is more robust to noise than the difference image.

  9. Hanging Out with Which Friends? Friendship-Level Predictors of Unstructured and Unsupervised Socializing in Adolescence

    Science.gov (United States)

    Siennick, Sonja E.; Osgood, D. Wayne

    2012-01-01

    Companions are central to explanations of the risky nature of unstructured and unsupervised socializing, yet we know little about whom adolescents are with when hanging out. We examine predictors of how often friendship dyads hang out via multilevel analyses of longitudinal friendship-level data on over 5,000 middle schoolers. Adolescents hang out most with their most available friends and their most generally similar friends, not with their most at-risk or similarly at-risk friends. These findings vary little by gender and wave. Together, the findings suggest that the risks of hanging out stem from the nature of hanging out as an activity, not the nature of adolescents’ companions, and that hanging out is a context for friends’ mutual reinforcement of pre-existing characteristics. PMID:23204811

  10. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

    Directory of Open Access Journals (Sweden)

    Angel Mur

    2016-04-01

    Full Text Available In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

  11. Modeling language and cognition with deep unsupervised learning: a tutorial overview

    Science.gov (United States)

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition. PMID:23970869

  12. A standardised individual unsupervised water exercise intervention for healthy pregnant women. A qualitative feasibility study

    DEFF Research Database (Denmark)

    Backhausen, Mette G; Katballe, Malene; Hansson, Helena

    2014-01-01

    INTRODUCTION: Low back pain during pregnancy is common and associated with sick leave. Studies suggest that exercise may reduce low back pain during pregnancy. Before carrying out a randomised controlled trail with individual water exercise as intervention a qualitative feasibility study was done....... OBJECTIVE: To explore women's views and experiences of the acceptability and benefits of and possible barriers to the standardised individual unsupervised water exercise intervention. MATERIALS AND METHODS: Eleven women were interviewed after participating in a water exercise intervention. Content analysis...... was used. RESULTS: Four main categories emerged: motivation to participate, attitudes towards the exercise programme, perception of benefits, and acceptability of supportive components. The women had a desire to stay physically active during pregnancy and found water exercise a suitable, type of exercise...

  13. Towards unsupervised polyaromatic hydrocarbons structural assignment from SA-TIMS-FTMS data.

    Science.gov (United States)

    Benigni, Paolo; Marin, Rebecca; Fernandez-Lima, Francisco

    2015-10-01

    With the advent of high resolution ion mobility analyzers and their coupling to ultrahigh resolution mass spectrometers, there is a need to further develop a theoretical workflow capable of correlating experimental accurate mass and mobility measurements with tridimensional candidate structures. In the present work, a general workflow is described for unsupervised tridimensional structural assignment based on accurate mass measurements, mobility measurements, in silico 2D-3D structure generation, and theoretical mobility calculations. In particular, the potential of this workflow will be shown for the analysis of polyaromatic hydrocarbons from Coal Tar SRM 1597a using selected accumulation - trapped ion mobility spectrometry (SA-TIMS) coupled to Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS). The proposed workflow can be adapted to different IMS scenarios, can utilize different collisional cross-section calculators and has the potential to include MS n and IMS n measurements for faster and more accurate tridimensional structural assignment.

  14. Unsupervised method for automatic construction of a disease dictionary from a large free text collection.

    Science.gov (United States)

    Xu, Rong; Supekar, Kaustubh; Morgan, Alex; Das, Amar; Garber, Alan

    2008-11-06

    Concept specific lexicons (e.g. diseases, drugs, anatomy) are a critical source of background knowledge for many medical language-processing systems. However, the rapid pace of biomedical research and the lack of constraints on usage ensure that such dictionaries are incomplete. Focusing on disease terminology, we have developed an automated, unsupervised, iterative pattern learning approach for constructing a comprehensive medical dictionary of disease terms from randomized clinical trial (RCT) abstracts, and we compared different ranking methods for automatically extracting con-textual patterns and concept terms. When used to identify disease concepts from 100 randomly chosen, manually annotated clinical abstracts, our disease dictionary shows significant performance improvement (F1 increased by 35-88%) over available, manually created disease terminologies.

  15. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

    Directory of Open Access Journals (Sweden)

    Stefano eAmbrogio

    2016-03-01

    Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

  16. Node clustering for wireless sensor networks

    International Nuclear Information System (INIS)

    Bhatti, S.; Qureshi, I.A.; Memon, S.

    2012-01-01

    Recent years have witnessed considerable growth in the development and deployment of clustering methods which are not only used to maintain network resources but also increases the reliability of the WSNs (Wireless Sensor Network) and the facts manifest by the wide range of clustering solutions. Node clustering by selecting key parameters to tackle the dynamic behaviour of resource constraint WSN is a challenging issue. This paper highlights the recent progress which has been carried out pertaining to the development of clustering solutions for the WSNs. The paper presents classification of node clustering methods and their comparison based on the objectives, clustering criteria and methodology. In addition, the potential open issues which need to be considered for future work are high lighted. Keywords: Clustering, Sensor Network, Static, Dynamic

  17. BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.

    Science.gov (United States)

    Hoff, Katharina J; Lange, Simone; Lomsadze, Alexandre; Borodovsky, Mark; Stanke, Mario

    2016-03-01

    Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  18. Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

    Science.gov (United States)

    Leibig, Christian; Wachtler, Thomas; Zeck, Günther

    2016-09-15

    Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysis.

    Science.gov (United States)

    Kanchanatawan, Buranee; Sriswasdi, Sira; Thika, Supaksorn; Stoyanov, Drozdstoy; Sirivichayakul, Sunee; Carvalho, André F; Geffard, Michel; Maes, Michael

    2018-05-23

    Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate classifications should be based on supervised and unsupervised learning rather than on consensus criteria. This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia. We found that using negative symptoms as discriminatory variables, schizophrenia patients may be divided into two distinct classes modelled by (A) impairments in IgA/IgM responses to noxious and generally more protective tryptophan catabolites, (B) impairments in episodic and semantic memory, paired associative learning and false memory creation, and (C) psychotic, excitation, hostility, mannerism, negative, and affective symptoms. The first cluster shows increased negative, psychotic, excitation, hostility, mannerism, depression and anxiety symptoms, and more neuroimmune and cognitive disorders and is therefore called "major neurocognitive psychosis" (MNP). The second cluster, called "simple neurocognitive psychosis" (SNP) is discriminated from normal controls by the same features although the impairments are less well developed than in MNP. The latter is additionally externally validated by lowered quality of life, body mass (reflecting a leptosome body type), and education (reflecting lower cognitive reserve). Previous distinctions including "type 1" (positive)/"type 2" (negative) and DSM-IV-TR (eg, paranoid) schizophrenia could not be validated using machine learning techniques. Previous names of the illness, including schizophrenia, are not very adequate because they do not describe the features of the illness, namely, interrelated neuroimmune, cognitive, and clinical features. Stable-phase schizophrenia consists of 2 relevant qualitatively distinct categories or nosological entities with SNP

  20. Optimization of a Non-traditional Unsupervised Classification Approach for Land Cover Analysis

    Science.gov (United States)

    Boyd, R. K.; Brumfield, J. O.; Campbell, W. J.

    1982-01-01

    The conditions under which a hybrid of clustering and canonical analysis for image classification produce optimum results were analyzed. The approach involves generation of classes by clustering for input to canonical analysis. The importance of the number of clusters input and the effect of other parameters of the clustering algorithm (ISOCLS) were examined. The approach derives its final result by clustering the canonically transformed data. Therefore the importance of number of clusters requested in this final stage was also examined. The effect of these variables were studied in terms of the average separability (as measured by transformed divergence) of the final clusters, the transformation matrices resulting from different numbers of input classes, and the accuracy of the final classifications. The research was performed with LANDSAT MSS data over the Hazleton/Berwick Pennsylvania area. Final classifications were compared pixel by pixel with an existing geographic information system to provide an indication of their accuracy.

  1. Testing methodologies

    Energy Technology Data Exchange (ETDEWEB)

    Bender, M.A.

    1990-01-01

    Several methodologies are available for screening human populations for exposure to ionizing radiation. Of these, aberration frequency determined in peripheral blood lymphocytes is the best developed. Individual exposures to large doses can easily be quantitated, and population exposures to occupational levels can be detected. However, determination of exposures to the very low doses anticipated from a low-level radioactive waste disposal site is more problematical. Aberrations occur spontaneously, without known cause. Exposure to radiation induces no new or novel types, but only increases their frequency. The limitations of chromosomal aberration dosimetry for detecting low level radiation exposures lie mainly in the statistical signal to noise'' problem, the distribution of aberrations among cells and among individuals, and the possible induction of aberrations by other environmental occupational or medical exposures. However, certain features of the human peripheral lymphocyte-chromosomal aberration system make it useful in screening for certain types of exposures. Future technical developments may make chromosomal aberration dosimetry more useful for low-level radiation exposures. Other methods, measuring gene mutations or even minute changes on the DNA level, while presently less will developed techniques, may eventually become even more practical and sensitive assays for human radiation exposure. 15 refs.

  2. Testing methodologies

    International Nuclear Information System (INIS)

    Bender, M.A.

    1990-01-01

    Several methodologies are available for screening human populations for exposure to ionizing radiation. Of these, aberration frequency determined in peripheral blood lymphocytes is the best developed. Individual exposures to large doses can easily be quantitated, and population exposures to occupational levels can be detected. However, determination of exposures to the very low doses anticipated from a low-level radioactive waste disposal site is more problematical. Aberrations occur spontaneously, without known cause. Exposure to radiation induces no new or novel types, but only increases their frequency. The limitations of chromosomal aberration dosimetry for detecting low level radiation exposures lie mainly in the statistical ''signal to noise'' problem, the distribution of aberrations among cells and among individuals, and the possible induction of aberrations by other environmental occupational or medical exposures. However, certain features of the human peripheral lymphocyte-chromosomal aberration system make it useful in screening for certain types of exposures. Future technical developments may make chromosomal aberration dosimetry more useful for low-level radiation exposures. Other methods, measuring gene mutations or even minute changes on the DNA level, while presently less will developed techniques, may eventually become even more practical and sensitive assays for human radiation exposure. 15 refs

  3. The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis

    Directory of Open Access Journals (Sweden)

    Chen Yidong

    2004-01-01

    Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.

  4. Cluster forcing

    DEFF Research Database (Denmark)

    Christensen, Thomas Budde

    The cluster theory attributed to Michael Porter has significantly influenced industrial policies in countries across Europe and North America since the beginning of the 1990s. Institutions such as the EU, OECD and the World Bank and governments in countries such as the UK, France, The Netherlands...... or management. Both the Accelerate Wales and the Accelerate Cluster programmes target this issue by trying to establish networks between companies that can be used to supply knowledge from research institutions to manufacturing companies. The paper concludes that public sector interventions can make...... businesses. The universities were not considered by the participating companies to be important parts of the local business environment and inputs from universities did not appear to be an important source to access knowledge about new product development or new techniques in production, distribution...

  5. Exploitation of Clustering Techniques in Transactional Healthcare Data

    Directory of Open Access Journals (Sweden)

    Naeem Ahmad Mahoto

    2014-03-01

    Full Text Available Healthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essential to manage the quality and standardization of treatment procedures. Exploratory data analysis, patternanalysis and grouping of data is managed using clustering techniques, which work as an unsupervised classification. A number of healthcare applications are developed that use several data mining techniques for classification, clustering and extracting useful information from healthcare data. The challenging issue in this domain is to select adequate data mining algorithm for optimal results. This paper exploits three different clustering algorithms: DBSCAN (Density-Based Clustering, agglomerative hierarchical and k-means in real transactional healthcare data of diabetic patients (taken as case study to analyse their performance in large and dispersed healthcare data. The best solution of cluster sets among the exploited algorithms is evaluated using clustering quality indexes and is selected to identify the possible subgroups of patients having similar treatment patterns

  6. Regional Innovation Clusters

    Data.gov (United States)

    Small Business Administration — The Regional Innovation Clusters serve a diverse group of sectors and geographies. Three of the initial pilot clusters, termed Advanced Defense Technology clusters,...

  7. Cluster analysis

    OpenAIRE

    Mucha, Hans-Joachim; Sofyan, Hizir

    2000-01-01

    As an explorative technique, duster analysis provides a description or a reduction in the dimension of the data. It classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of many variables. Its aim is to construct groups in such a way that the profiles of objects in the same groups are relatively homogenous whereas the profiles of objects in different groups are relatively heterogeneous. Clustering is distinct from classification techniques, ...

  8. A protocol for a three-arm cluster randomized controlled superiority trial investigating the effects of two pedagogical methodologies in Swedish preschool settings on language and communication, executive functions, auditive selective attention, socioemotional skills and early maths skills.

    Science.gov (United States)

    Gerholm, Tove; Hörberg, Thomas; Tonér, Signe; Kallioinen, Petter; Frankenberg, Sofia; Kjällander, Susanne; Palmer, Anna; Taguchi, Hillevi Lenz

    2018-06-19

    During the preschool years, children develop abilities and skills in areas crucial for later success in life. These abilities include language, executive functions, attention, and socioemotional skills. The pedagogical methods used in preschools hold the potential to enhance these abilities, but our knowledge of which pedagogical practices aid which abilities, and for which children, is limited. The aim of this paper is to describe an intervention study designed to evaluate and compare two pedagogical methodologies in terms of their effect on the above-mentioned skills in Swedish preschool children. The study is a randomized control trial (RCT) where two pedagogical methodologies were tested to evaluate how they enhanced children's language, executive functions and attention, socioemotional skills, and early maths skills during an intensive 6-week intervention. Eighteen preschools including 28 units and 432 children were enrolled in a municipality close to Stockholm, Sweden. The children were between 4;0 and 6;0 years old and each preschool unit was randomly assigned to either of the interventions or to the control group. Background information on all children was collected via questionnaires completed by parents and preschools. Pre- and post-intervention testing consisted of a test battery including tests on language, executive functions, selective auditive attention, socioemotional skills and early maths skills. The interventions consisted of 6 weeks of intensive practice of either a socioemotional and material learning paradigm (SEMLA), for which group-based activities and interactional structures were the main focus, or an individual, digitally implemented attention and math training paradigm, which also included a set of self-regulation practices (DIL). All preschools were evaluated with the ECERS-3. If this intervention study shows evidence of a difference between group-based learning paradigms and individual training of specific skills in terms of

  9. Fuzzy clustering-based segmented attenuation correction in whole-body PET

    CERN Document Server

    Zaidi, H; Boudraa, A; Slosman, DO

    2001-01-01

    Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

  10. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

    Science.gov (United States)

    Lee, Alexandra J; Chang, Ivan; Burel, Julie G; Lindestam Arlehamn, Cecilia S; Mandava, Aishwarya; Weiskopf, Daniela; Peters, Bjoern; Sette, Alessandro; Scheuermann, Richard H; Qian, Yu

    2018-04-17

    Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and

  11. Implementasi KD-Tree K-Means Clustering untuk Klasterisasi Dokumen

    Directory of Open Access Journals (Sweden)

    Eric Budiman Gosno

    2013-09-01

    Full Text Available Klasterisasi dokumen adalah suatu proses pengelompokan dokumen secara otomatis dan unsupervised. Klasterisasi dokumen merupakan permasalahan yang sering ditemui dalam berbagai bidang seperti text mining dan sistem temu kembali informasi. Metode klasterisasi dokumen yang memiliki akurasi dan efisiensi waktu yang tinggi sangat diperlukan untuk meningkatkan hasil pada mesin pencari web,  dan untuk proses filtering. Salah satu metode klasterisasi yang telah dikenal dan diaplikasikan dalam klasterisasi dokumen adalah K-Means Clustering. Tetapi K-Means Clustering sensitif terhadap pemilihan posisi awal dari titik tengah klaster sehingga pemilihan posisi awal dari titik tengah klaster yang buruk akan mengakibatkan K-Means Clustering terjebak dalam local optimum. KD-Tree K-Means Clustering merupakan perbaikan dari K-Means Clustering. KD-Tree K-Means Clustering menggunakan struktur data K-Dimensional Tree dan nilai kerapatan pada proses inisialisasi titik tengah klaster. Pada makalah ini diimplementasikan algoritma KD-Tree K-Means Clustering untuk permasalahan klasterisasi dokumen. Performa klasterisasi dokumen yang dihasilkan oleh metode KD-Tree K-Means Clustering pada data set 20 newsgroup memiliki nilai distorsi 3×105 lebih rendah dibandingkan dengan nilai rerata distorsi K-Means Clustering dan nilai NIG 0,09 lebih baik dibandingkan dengan nilai NIG K-Means Clustering.

  12. Preliminary hard and soft bottom seafloor substrate map derived from an unsupervised classification of gridded backscatter and bathymetry derivatives of Ni'ihau Island, Hawaii, USA.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives of Ni'ihau Island,...

  13. CRED Preliminary hard and soft bottom seafloor substrate map derived from an unsupervised classification of gridded backscatter and bathymetry derivatives at the U.S. Territory of Guam.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at the U.S. Territory...

  14. Preliminary hard and soft bottom seafloor substrate map derived from an unsupervised classification of gridded backscatter and bathymetry derivatives at Swains Island, Territory of American Samoa, USA.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives at Swains Island,...

  15. Preliminary hard and soft bottom seafloor substrate map derived from an unsupervised classification of gridded backscatter and bathymetry derivatives at Tutuila Island, American Samoa, South Pacific.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at Tutuila Island,...

  16. The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

    Science.gov (United States)

    Masalmah, Yahya M.; Vélez-Reyes, Miguel

    2007-04-01

    The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

  17. Nuclear clustering - a cluster core model study

    International Nuclear Information System (INIS)

    Paul Selvi, G.; Nandhini, N.; Balasubramaniam, M.

    2015-01-01

    Nuclear clustering, similar to other clustering phenomenon in nature is a much warranted study, since it would help us in understanding the nature of binding of the nucleons inside the nucleus, closed shell behaviour when the system is highly deformed, dynamics and structure at extremes. Several models account for the clustering phenomenon of nuclei. We present in this work, a cluster core model study of nuclear clustering in light mass nuclei

  18. Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

    Directory of Open Access Journals (Sweden)

    Lei Ma

    2016-09-01

    Full Text Available Object-based change detection (OBCD has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

  19. Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

    Science.gov (United States)

    Sadeghi, Zahra; Testolin, Alberto

    2017-08-01

    In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

  20. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

    Directory of Open Access Journals (Sweden)

    Yansheng Li

    2016-08-01

    Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

  1. AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Vishwambhar Pathak

    2012-11-01

    Full Text Available Recent efforts in modeling of dynamics of the natural immune cells leading to artificial immune systems (AIS have ignited contemporary research interest in finding out its analogies to real world problems. The AIS models have been vastly exploited to develop dependable robust
    solutions to clustering. Most of the traditional clustering methods bear limitations in their capability to detect clusters of arbitrary shapes in a fully unsupervised manner. In this paper the recognition and communication dynamics of T Cell Receptors, the recognizing elements in innate immune
    system, has been modeled with a kernel density estimation method. The model has been shown to successfully discover non spherical clusters in spatial patterns. Modeling the cohesion of the antibodies and pathogens with ‘local influence’ measure inducts comprehensive extension of the
    antibody representation ball (ARB, which in turn corresponds to controlled expansion of clusters and prevents overfitting.

  2. From image captioning to video summary using deep recurrent networks and unsupervised segmentation

    Science.gov (United States)

    Morosanu, Bogdan-Andrei; Lemnaru, Camelia

    2018-04-01

    Automatic captioning systems based on recurrent neural networks have been tremendously successful at providing realistic natural language captions for complex and varied image data. We explore methods for adapting existing models trained on large image caption data sets to a similar problem, that of summarising videos using natural language descriptions and frame selection. These architectures create internal high level representations of the input image that can be used to define probability distributions and distance metrics on these distributions. Specifically, we interpret each hidden unit inside a layer of the caption model as representing the un-normalised log probability of some unknown image feature of interest for the caption generation process. We can then apply well understood statistical divergence measures to express the difference between images and create an unsupervised segmentation of video frames, classifying consecutive images of low divergence as belonging to the same context, and those of high divergence as belonging to different contexts. To provide a final summary of the video, we provide a group of selected frames and a text description accompanying them, allowing a user to perform a quick exploration of large unlabeled video databases.

  3. Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

    Science.gov (United States)

    Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

    2017-12-01

    Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

  4. Analysis On Land Cover In Municipality Of Malang With Landsat 8 Image Through Unsupervised Classification

    Science.gov (United States)

    Nahari, R. V.; Alfita, R.

    2018-01-01

    Remote sensing technology has been widely used in the geographic information system in order to obtain data more quickly, accurately and affordably. One of the advantages of using remote sensing imagery (satellite imagery) is to analyze land cover and land use. Satellite image data used in this study were images from the Landsat 8 satellite combined with the data from the Municipality of Malang government. The satellite image was taken in July 2016. Furthermore, the method used in this study was unsupervised classification. Based on the analysis towards the satellite images and field observations, 29% of the land in the Municipality of Malang was plantation, 22% of the area was rice field, 12% was residential area, 10% was land with shrubs, and the remaining 2% was water (lake/reservoir). The shortcoming of the methods was 25% of the land in the area was unidentified because it was covered by cloud. It is expected that future researchers involve cloud removal processing to minimize unidentified area.

  5. Unsupervised symmetrical trademark image retrieval in soccer telecast using wavelet energy and quadtree decomposition

    Science.gov (United States)

    Ong, Swee Khai; Lim, Wee Keong; Soo, Wooi King

    2013-04-01

    Trademark, a distinctive symbol, is used to distinguish products or services provided by a particular person, group or organization from other similar entries. As trademark represents the reputation and credit standing of the owner, it is important to differentiate one trademark from another. Many methods have been proposed to identify, classify and retrieve trademarks. However, most methods required features database and sample sets for training prior to recognition and retrieval process. In this paper, a new feature on wavelet coefficients, the localized wavelet energy, is introduced to extract features of trademarks. With this, unsupervised content-based symmetrical trademark image retrieval is proposed without the database and prior training set. The feature analysis is done by an integration of the proposed localized wavelet energy and quadtree decomposed regional symmetrical vector. The proposed framework eradicates the dependence on query database and human participation during the retrieval process. In this paper, trademarks for soccer games sponsors are the intended trademark category. Video frames from soccer telecast are extracted and processed for this study. Reasonably good localization and retrieval results on certain categories of trademarks are achieved. A distinctive symbol is used to distinguish products or services provided by a particular person, group or organization from other similar entries.

  6. Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.

    Science.gov (United States)

    Huang, Yue; Zheng, Han; Liu, Chi; Ding, Xinghao; Rohde, Gustavo K

    2017-11-01

    Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications. A very simple but effective deep learning method, that introduces the concept of unsupervised domain adaptation to a simple convolutional neural network (CNN), has been proposed in this paper. Inspired by transfer learning, our paper assumes that the training data and testing data follow different distributions, and there is an adaptation operation to more accurately estimate the kernels in CNN in feature extraction, in order to enhance performance by transferring knowledge from labeled data in source domain to unlabeled data in target domain. The model has been evaluated using three independent public epithelium-stroma datasets by cross-dataset validations. The experimental results demonstrate that for epithelium-stroma classification, the proposed framework outperforms the state-of-the-art deep neural network model, and it also achieves better performance than other existing deep domain adaptation methods. The proposed model can be considered to be a better option for real-world applications in histopathological image analysis, since there is no longer a requirement for large-scale labeled data in each specified domain.

  7. An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

    Science.gov (United States)

    Shamwell, E Jared; Nothwang, William D; Perlis, Donald

    2018-05-04

    Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

  8. CAPES: Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to nd the optimal values of tunable parameters in computer systems, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system’s state, and trains a DNN which uses Q-learning to suggest changes to the system’s current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system’s daily operation. Evaluation of a prototype on a Lustre system demonstrates an increase in I/O throughput up to 45% at saturation point. About the...

  9. Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction

    Directory of Open Access Journals (Sweden)

    Che Ngufor

    2013-01-01

    Full Text Available The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets.

  10. Unsupervised obstacle detection in driving environments using deep-learning-based stereovision

    KAUST Repository

    Dairi, Abdelkader; Harrou, Fouzi; Senouci, Mohamed; Sun, Ying

    2017-01-01

    A vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches. Here, we design an innovative hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE). This hybrid auto-encode (HAE) model combines the greedy learning features of DBM with the dimensionality reduction capacity of AE to accurately and reliably detect the presence of obstacles. We combine the proposed hybrid model with the one-class support vector machines (OCSVM) to visually monitor an urban scene. We also propose an efficient approach to estimating obstacles location and track their positions via scene densities. Specifically, we address obstacle detection as an anomaly detection problem. If an obstacle is detected by the OCSVM algorithm, then localization and tracking algorithm is executed. We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD). Results show the capacity of the proposed approach to reliably detect obstacles.

  11. Unsupervised quantification of abdominal fat from CT images using Greedy Snakes

    Science.gov (United States)

    Agarwal, Chirag; Dallal, Ahmed H.; Arbabshirani, Mohammad R.; Patel, Aalpen; Moore, Gregory

    2017-02-01

    Adipose tissue has been associated with adverse consequences of obesity. Total adipose tissue (TAT) is divided into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Intra-abdominal fat (VAT), located inside the abdominal cavity, is a major factor for the classic obesity related pathologies. Since direct measurement of visceral and subcutaneous fat is not trivial, substitute metrics like waist circumference (WC) and body mass index (BMI) are used in clinical settings to quantify obesity. Abdominal fat can be assessed effectively using CT or MRI, but manual fat segmentation is rather subjective and time-consuming. Hence, an automatic and accurate quantification tool for abdominal fat is needed. The goal of this study is to extract TAT, VAT and SAT fat from abdominal CT in a fully automated unsupervised fashion using energy minimization techniques. We applied a four step framework consisting of 1) initial body contour estimation, 2) approximation of the body contour, 3) estimation of inner abdominal contour using Greedy Snakes algorithm, and 4) voting, to segment the subcutaneous and visceral fat. We validated our algorithm on 952 clinical abdominal CT images (from 476 patients with a very wide BMI range) collected from various radiology departments of Geisinger Health System. To our knowledge, this is the first study of its kind on such a large and diverse clinical dataset. Our algorithm obtained a 3.4% error for VAT segmentation compared to manual segmentation. These personalized and accurate measurements of fat can complement traditional population health driven obesity metrics such as BMI and WC.

  12. Indoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbation

    KAUST Repository

    Majeed, Khaqan; Sorour, Sameh; Al-Naffouri, Tareq Y.; Valaee, Shahrokh

    2015-01-01

    The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15% of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1% of the fingerprinting load, some crowd sourced readings and plan coordinates of the indoor area. The 1% fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50% performance improvement by using this information as compared to using only fingerprints.

  13. A standardised individual unsupervised water exercise intervention for healthy pregnant women. A qualitative feasibility study.

    Science.gov (United States)

    Backhausen, Mette G; Katballe, Malene; Hansson, Helena; Tabor, Ann; Damm, Peter; Hegaard, Hanne K

    2014-12-01

    Low back pain during pregnancy is common and associated with sick leave. Studies suggest that exercise may reduce low back pain during pregnancy. Before carrying out a randomised controlled trail with individual water exercise as intervention a qualitative feasibility study was done. To explore women's views and experiences of the acceptability and benefits of and possible barriers to the standardised individual unsupervised water exercise intervention. Eleven women were interviewed after participating in a water exercise intervention. Content analysis was used. Four main categories emerged: motivation to participate, attitudes towards the exercise programme, perception of benefits, and acceptability of supportive components. The women had a desire to stay physically active during pregnancy and found water exercise a suitable, type of exercise to perform during pregnancy. The intervention was experienced to have benefits on both their physical health and their mental well-being. Crowded swimming pools were perceived as the greatest barrier. It is feasible to perform a RCT using the described intervention. The intervention was accepted by the participants because it supported their desire to be physically active during pregnancy. The main barrier was crowded swimming pools and this issue must be addressed in a future RCT. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Indoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbation

    KAUST Repository

    Majeed, Khaqan

    2015-12-22

    The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15% of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1% of the fingerprinting load, some crowd sourced readings and plan coordinates of the indoor area. The 1% fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50% performance improvement by using this information as compared to using only fingerprints.

  15. Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

    Directory of Open Access Journals (Sweden)

    Stefanos Georganos

    2018-02-01

    Full Text Available In object-based image analysis (OBIA, the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimization (USPO. A popular USPO method does this through the optimization of a “global score” (GS, which minimizes intrasegment heterogeneity and maximizes intersegment heterogeneity. However, the calculated GS values are sensitive to the minimum and maximum ranges of the candidate segmentations. Previous research proposed the use of fixed minimum/maximum threshold values for the intrasegment/intersegment heterogeneity measures to deal with the sensitivity of user-defined ranges, but the performance of this approach has not been investigated in detail. In the context of a remote sensing very-high-resolution urban application, we show the limitations of the fixed threshold approach, both in a theoretical and applied manner, and instead propose a novel solution to identify the range of candidate segmentations using local regression trend analysis. We found that the proposed approach showed significant improvements over the use of fixed minimum/maximum values, is less subjective than user-defined threshold values and, thus, can be of merit for a fully automated procedure and big data applications.

  16. Fusion of footsteps and face biometrics on an unsupervised and uncontrolled environment

    Science.gov (United States)

    Vera-Rodriguez, Ruben; Tome, Pedro; Fierrez, Julian; Ortega-Garcia, Javier

    2012-06-01

    This paper reports for the first time experiments on the fusion of footsteps and face on an unsupervised and not controlled environment for person authentication. Footstep recognition is a relatively new biometric based on signals extracted from people walking over floor sensors. The idea of the fusion between footsteps and face starts from the premise that in an area where footstep sensors are installed it is very simple to place a camera to capture also the face of the person that walks over the sensors. This setup may find application in scenarios like ambient assisted living, smart homes, eldercare, or security access. The paper reports a comparative assessment of both biometrics using the same database and experimental protocols. In the experimental work we consider two different applications: smart homes (small group of users with a large set of training data) and security access (larger group of users with a small set of training data) obtaining results of 0.9% and 5.8% EER respectively for the fusion of both modalities. This is a significant performance improvement compared with the results obtained by the individual systems.

  17. Online unsupervised formation of cell assemblies for the encoding of multiple cognitive maps.

    Science.gov (United States)

    Salihoglu, Utku; Bersini, Hugues; Yamaguchi, Yoko; Molter, Colin

    2009-01-01

    Since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories. However, the mechanisms underlying their formation are poorly understood and, so far, there is no biologically plausible algorithms which can explain how external stimuli can be online stored in cell assemblies. We addressed this question in a previous paper [Salihoglu, U., Bersini, H., Yamaguchi, Y., Molter, C., (2009). A model for the cognitive map formation: Application of the retroaxonal theory. In Proc. IEEE international joint conference on neural networks], were, based on biologically plausible mechanisms, a novel unsupervised algorithm for online cell assemblies' creation was developed. The procedure involved simultaneously, a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilized the cell assemblies by learning the feedforward input connections. Here, we first quantify the role played by the retroaxonal feedback mechanism. Then, we show how multiple cognitive maps, composed by a set of orthogonal input stimuli, can be encoded in the network. As a result, when facing a previously learned input, the system is able to retrieve the cognitive map it belongs to. As a consequence, ambiguous inputs which could belong to multiple cognitive maps can be disambiguated by the knowledge of the context, i.e. the cognitive map.

  18. A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes

    Science.gov (United States)

    Sato, Daisuke; Hanaoka, Shouhei; Nomura, Yukihiro; Takenaga, Tomomi; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

    2018-02-01

    Purpose: The target disorders of emergency head CT are wide-ranging. Therefore, people working in an emergency department desire a computer-aided detection system for general disorders. In this study, we proposed an unsupervised anomaly detection method in emergency head CT using an autoencoder and evaluated the anomaly detection performance of our method in emergency head CT. Methods: We used a 3D convolutional autoencoder (3D-CAE), which contains 11 layers in the convolution block and 6 layers in the deconvolution block. In the training phase, we trained the 3D-CAE using 10,000 3D patches extracted from 50 normal cases. In the test phase, we calculated abnormalities of each voxel in 38 emergency head CT volumes (22 abnormal cases and 16 normal cases) for evaluation and evaluated the likelihood of lesion existence. Results: Our method achieved a sensitivity of 68% and a specificity of 88%, with an area under the curve of the receiver operating characteristic curve of 0.87. It shows that this method has a moderate accuracy to distinguish normal CT cases to abnormal ones. Conclusion: Our method has potentialities for anomaly detection in emergency head CT.

  19. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

    Science.gov (United States)

    Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

    2016-05-01

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

  20. Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data

    Science.gov (United States)

    Niezgoda, Stephen R.; Glover, Jared

    2013-11-01

    The estimation of orientation distribution functions (ODFs) from discrete orientation data, as produced by electron backscatter diffraction or crystal plasticity micromechanical simulations, is typically achieved via techniques such as the Williams-Imhof-Matthies-Vinel (WIMV) algorithm or generalized spherical harmonic expansions, which were originally developed for computing an ODF from pole figures measured by X-ray or neutron diffraction. These techniques rely on ad-hoc methods for choosing parameters, such as smoothing half-width and bandwidth, and for enforcing positivity constraints and appropriate normalization. In general, such approaches provide little or no information-theoretic guarantees as to their optimality in describing the given dataset. In the current study, an unsupervised learning algorithm is proposed which uses a finite mixture of Bingham distributions for the estimation of ODFs from discrete orientation data. The Bingham distribution is an antipodally-symmetric, max-entropy distribution on the unit quaternion hypersphere. The proposed algorithm also introduces a minimum message length criterion, a common tool in information theory for balancing data likelihood with model complexity, to determine the number of components in the Bingham mixture. This criterion leads to ODFs which are less likely to overfit (or underfit) the data, eliminating the need for a priori parameter choices.

  1. Unsupervised obstacle detection in driving environments using deep-learning-based stereovision

    KAUST Repository

    Dairi, Abdelkader

    2017-12-06

    A vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches. Here, we design an innovative hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE). This hybrid auto-encode (HAE) model combines the greedy learning features of DBM with the dimensionality reduction capacity of AE to accurately and reliably detect the presence of obstacles. We combine the proposed hybrid model with the one-class support vector machines (OCSVM) to visually monitor an urban scene. We also propose an efficient approach to estimating obstacles location and track their positions via scene densities. Specifically, we address obstacle detection as an anomaly detection problem. If an obstacle is detected by the OCSVM algorithm, then localization and tracking algorithm is executed. We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD). Results show the capacity of the proposed approach to reliably detect obstacles.

  2. Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

    Science.gov (United States)

    Matsubara, Takashi

    2017-01-01

    Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

  3. A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

    Science.gov (United States)

    Fiannaca, Antonino; La Rosa, Massimo; Rizzo, Riccardo; Urso, Alfonso

    2015-07-01

    In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Supervised and Unsupervised Self-Testing for HIV in High- and Low-Risk Populations: A Systematic Review

    Science.gov (United States)

    Pant Pai, Nitika; Sharma, Jigyasa; Shivkumar, Sushmita; Pillay, Sabrina; Vadnais, Caroline; Joseph, Lawrence; Dheda, Keertan; Peeling, Rosanna W.

    2013-01-01

    Background Stigma, discrimination, lack of privacy, and long waiting times partly explain why six out of ten individuals living with HIV do not access facility-based testing. By circumventing these barriers, self-testing offers potential for more people to know their sero-status. Recent approval of an in-home HIV self test in the US has sparked self-testing initiatives, yet data on acceptability, feasibility, and linkages to care are limited. We systematically reviewed evidence on supervised (self-testing and counselling aided by a health care professional) and unsupervised (performed by self-tester with access to phone/internet counselling) self-testing strategies. Methods and Findings Seven databases (Medline [via PubMed], Biosis, PsycINFO, Cinahl, African Medicus, LILACS, and EMBASE) and conference abstracts of six major HIV/sexually transmitted infections conferences were searched from 1st January 2000–30th October 2012. 1,221 citations were identified and 21 studies included for review. Seven studies evaluated an unsupervised strategy and 14 evaluated a supervised strategy. For both strategies, data on acceptability (range: 74%–96%), preference (range: 61%–91%), and partner self-testing (range: 80%–97%) were high. A high specificity (range: 99.8%–100%) was observed for both strategies, while a lower sensitivity was reported in the unsupervised (range: 92.9%–100%; one study) versus supervised (range: 97.4%–97.9%; three studies) strategy. Regarding feasibility of linkage to counselling and care, 96% (n = 102/106) of individuals testing positive for HIV stated they would seek post-test counselling (unsupervised strategy, one study). No extreme adverse events were noted. The majority of data (n = 11,019/12,402 individuals, 89%) were from high-income settings and 71% (n = 15/21) of studies were cross-sectional in design, thus limiting our analysis. Conclusions Both supervised and unsupervised testing strategies were highly acceptable

  5. Unsupervised tattoo segmentation combining bottom-up and top-down cues

    Science.gov (United States)

    Allen, Josef D.; Zhao, Nan; Yuan, Jiangbo; Liu, Xiuwen

    2011-06-01

    Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a figureground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is efficient and suitable for further tattoo classification and retrieval purpose.

  6. Cluster headache

    Directory of Open Access Journals (Sweden)

    Ducros Anne

    2008-07-01

    Full Text Available Abstract Cluster headache (CH is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye. It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name in bouts that can occur during specific months of the year. Alcohol is the only dietary trigger of CH, strong odors (mainly solvents and cigarette smoke and napping may also trigger CH attacks. During bouts, attacks may happen at precise hours, especially during the night. During the attacks, patients tend to be restless. CH may be episodic or chronic, depending on the presence of remission periods. CH is associated with trigeminovascular activation and neuroendocrine and vegetative disturbances, however, the precise cautive mechanisms remain unknown. Involvement of the hypothalamus (a structure regulating endocrine function and sleep-wake rhythms has been confirmed, explaining, at least in part, the cyclic aspects of CH. The disease is familial in about 10% of cases. Genetic factors play a role in CH susceptibility, and a causative role has been suggested for the hypocretin receptor gene. Diagnosis is clinical. Differential diagnoses include other primary headache diseases such as migraine, paroxysmal hemicrania and SUNCT syndrome. At present, there is no curative treatment. There are efficient treatments to shorten the painful attacks (acute treatments and to reduce the number of daily attacks (prophylactic treatments. Acute treatment is based on subcutaneous administration of sumatriptan and high-flow oxygen. Verapamil, lithium, methysergide, prednisone, greater occipital nerve blocks and topiramate may be used for prophylaxis. In refractory cases, deep-brain stimulation of the

  7. Methodological Problems of Nanotechnoscience

    Science.gov (United States)

    Gorokhov, V. G.

    Recently, we have reported on the definitions of nanotechnology as a new type of NanoTechnoScience and on the nanotheory as a cluster of the different natural and engineering theories. Nanotechnology is not only a new type of scientific-engineering discipline, but it evolves also in a “nonclassical” way. Nanoontology or nano scientific world view has a function of the methodological orientation for the choice the theoretical means and methods toward a solution to the scientific and engineering problems. This allows to change from one explanation and scientific world view to another without any problems. Thus, nanotechnology is both a field of scientific knowledge and a sphere of engineering activity, in other words, NanoTechnoScience is similar to Systems Engineering as the analysis and design of large-scale, complex, man/machine systems but micro- and nanosystems. Nano systems engineering as well as Macro systems engineering includes not only systems design but also complex research. Design orientation has influence on the change of the priorities in the complex research and of the relation to the knowledge, not only to “the knowledge about something”, but also to the knowledge as the means of activity: from the beginning control and restructuring of matter at the nano-scale is a necessary element of nanoscience.

  8. Designing ordering and inventory management methodologies for purchased parts

    NARCIS (Netherlands)

    de Boer, L.; Looman, Arnold; Ruffini, F.A.J.

    2002-01-01

    This article presents a method for redesigning the ordering and inventory management methodologies for purchased parts in a manufacturing firm. The method takes the perspective of the purchasing and logistics manager, defines clusters of purchased items, and subsequently assigns each cluster to a

  9. Clinical trial methodology

    National Research Council Canada - National Science Library

    Peace, Karl E; Chen, Ding-Geng

    2011-01-01

    ... in the pharmaceutical industry, Clinical trial methodology emphasizes the importance of statistical thinking in clinical research and presents the methodology as a key component of clinical research...

  10. Brightest Cluster Galaxies in REXCESS Clusters

    Science.gov (United States)

    Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

    2009-01-01

    Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

  11. ADAPTIVE WEB SITE DENGAN METODE FUZZY CLUSTERING

    Directory of Open Access Journals (Sweden)

    Muchammad Husni

    2004-01-01

    Full Text Available Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Ledakan pertumbuhan dan perkembangan informasi dalam dunia maya menjadikan personalisasian informasi menjadi isu yang penting. Personalisasi informasi yang akan diberikan oleh situs web akan sangat mempengaruhi pola dan perilaku pengguna dalam pencarian informasi, terutama pada perdagangan elektronis (e-commerce. Salah satu pendekatan yang memungkinkan dalam personalisasian web adalah mencari profil pengguna (user profile dari data historis yang sangat besar di file web log. Pengklasifikasian data tanpa pengawasan (unsupervised clasification atau metode metode clustering cukup baik untuk menganalisa data log akses pengguna yang semi terstruktur. Pada metode ini, didefinisikan "user session" dan juga ukuran perbedaan (dissimilarity diantara dua web session yang menggambarkan pengorganisasian sebuah web site. Untuk mendapatkan sebuah profil akses pengguna, dilakukan pembagian user session berdasarkan pasangan ketidaksamaan menggunakan algoritma Fuzzy Clustering. Kata kunci : Adaptive Website, Fuzzy Clustering, personalisasi informasi.

  12. Unsupervised image segmentation for passive THz broadband images for concealed weapon detection

    Science.gov (United States)

    Ramírez, Mabel D.; Dietlein, Charles R.; Grossman, Erich; Popović, Zoya

    2007-04-01

    This work presents the application of a basic unsupervised classification algorithm for the segmentation of indoor passive Terahertz images. The 30,000 pixel broadband images of a person with concealed weapons under clothing are taken at a range of 0.8-2m over a frequency range of 0.1-1.2THz using single-pixel row-based raster scanning. The spiral-antenna coupled 36x1x0.02μm Nb bridge cryogenic micro-bolometers are developed at NIST-Optoelectronics Division. The antenna is evaporated on a 250μm thick Si substrate with a 4mm diameter hyper-hemispherical Si lens. The NETD of the microbolometer is 125mK at an integration time of 30 ms. The background temperature calibration is performed with a known 25 pixel source above 330 K, and a measured background fluctuation of 200-500mK. Several weapons were concealed under different fabrics: cotton, polyester, windblocker jacket and thermal sweater. Measured temperature contrasts ranged from 0.5-1K for wrinkles in clothing to 5K for a zipper and 8K for the concealed weapon. In order to automate feature detection in the images, some image processing and pattern recognition techniques have been applied and the results are presented here. We show that even simple algorithms, that can potentially be performed in real time, are capable of differentiating between a metal and a dielectric object concealed under clothing. Additionally, we show that pre-processing can reveal low temperature contrast features, such as folds in clothing.

  13. Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm

    Science.gov (United States)

    Makin, Joseph G.; O'Doherty, Joseph E.; Cardoso, Mariana M. B.; Sabes, Philip N.

    2018-04-01

    Objective. The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity—vectors of spike counts in small temporal windows—as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip. Inferring the state from the observations then takes the form of a dynamical filter, typically some variant on Kalman’s (KF). The KF, however, although fairly robust in practice, is optimal only when the relationships between variables are linear and the noise is Gaussian, conditions usually violated in practice. Approach. To overcome these limitations we introduce a new filter, the ‘recurrent exponential-family harmonium’ (rEFH), that models the spike counts explicitly as Poisson-distributed, and allows for arbitrary nonlinear dynamics and observation models. Furthermore, the model underlying the filter is acquired through unsupervised learning, which allows temporal correlations in spike counts to be explained by latent dynamics that do not necessarily correspond to the kinematic state of the fingertip. Main results. We test the rEFH on offline reconstruction of the kinematics of reaches in the plane. The rEFH outperforms the standard, as well as three other state-of-the-art, decoders, across three monkeys, two different tasks, most kinematic variables, and a range of bin widths, amounts of training data, and numbers of neurons. Significance. Our algorithm establishes a new state of the art for offline decoding of reaches—in particular, for fingertip velocities, the variable used for control in most online decoders.

  14. Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.

    Science.gov (United States)

    Hansen, Mirko; Zahari, Finn; Ziegler, Martin; Kohlstedt, Hermann

    2017-01-01

    The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al 2 O 3 /Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al 2 O 3 tunnel barrier and a 2.5 mm thick Nb x O y memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong I - V non-linearity might avoid the need for selector devices in crossbar array structures.

  15. Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

    Science.gov (United States)

    Srinivasa, Narayan; Cho, Youngkwan

    2014-01-01

    A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  16. UrQt: an efficient software for the Unsupervised Quality trimming of NGS data.

    Science.gov (United States)

    Modolo, Laurent; Lerat, Emmanuelle

    2015-04-29

    Quality control is a necessary step of any Next Generation Sequencing analysis. Although customary, this step still requires manual interventions to empirically choose tuning parameters according to various quality statistics. Moreover, current quality control procedures that provide a "good quality" data set, are not optimal and discard many informative nucleotides. To address these drawbacks, we present a new quality control method, implemented in UrQt software, for Unsupervised Quality trimming of Next Generation Sequencing reads. Our trimming procedure relies on a well-defined probabilistic framework to detect the best segmentation between two segments of unreliable nucleotides, framing a segment of informative nucleotides. Our software only requires one user-friendly parameter to define the minimal quality threshold (phred score) to consider a nucleotide to be informative, which is independent of both the experiment and the quality of the data. This procedure is implemented in C++ in an efficient and parallelized software with a low memory footprint. We tested the performances of UrQt compared to the best-known trimming programs, on seven RNA and DNA sequencing experiments and demonstrated its optimality in the resulting tradeoff between the number of trimmed nucleotides and the quality objective. By finding the best segmentation to delimit a segment of good quality nucleotides, UrQt greatly increases the number of reads and of nucleotides that can be retained for a given quality objective. UrQt source files, binary executables for different operating systems and documentation are freely available (under the GPLv3) at the following address: https://lbbe.univ-lyon1.fr/-UrQt-.html .

  17. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Peter U. Diehl

    2015-08-01

    Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

  18. Unsupervised detection, quantification and localization of white matter hyper-intensities in magnetic resonance imaging

    International Nuclear Information System (INIS)

    Gicquel, Sebastien

    1998-01-01

    White matter hyper-intensities (WMH) are punctual ischemic areas, related to arteriosclerosis, visible on T2- and PD-weighted MRI. They are present on elderly normal subjects, and their proportion vary between 27 to 83 % from study to study. This strong variability in their counting is especially caused by human observation, so an unsupervised detection tool is necessary. The method is based on the analysis of Tl-weighted MRI in sagittal orientation, and of T2- and PD-weighted MRI in transaxial orientation of a single subject. Detection of WMH is based on a segmentation phase, and contains a large number of pre- and post-processing. First, a correction of inhomogeneities due to instrumentation is applied, with the modelization of the intensity variations appearing on a homogenous object. and the Computing a correction matrix. The three MRI modalities are then placed in a single referential, using a MRI-PET registration method. The brain is segmented on the Tl-weighted MRI, resulting in a mask used to segment the two others modalities after registration. A multispectral bayesian segmentation algorithm extracts the white matter (WM) and objects likely to be WMH. A set of morphological operations generate an image representing WM and potential WMH. A second segmentation of this image, in two classes, results in a WMH mask. It is then possible to list the WMH, their size, their shape, and their position in the MRI referential. At last, the coordinates of the WMH centers of mass are placed into Talairach space, allowing statistical analysis on a large number of subjects. This method has been in part applied on a data base of 850 subjects. The four steps (correction of inhomogeneities, registration, segmentation of the brain and detection of AC and PC landmarks) result in a global success rate of 90 %. (author) [fr

  19. Recent progresses of neural network unsupervised learning: I. Independent component analyses generalizing PCA

    Science.gov (United States)

    Szu, Harold H.

    1999-03-01

    The early vision principle of redundancy reduction of 108 sensor excitations is understandable from computer vision viewpoint toward sparse edge maps. It is only recently derived using a truly unsupervised learning paradigm of artificial neural networks (ANN). In fact, the biological vision, Hubel- Wiesel edge maps, is reproduced seeking the underlying independent components analyses (ICA) among 102 image samples by maximizing the ANN output entropy (partial)H(V)/(partial)[W] equals (partial)[W]/(partial)t. When a pair of newborn eyes or ears meet the bustling and hustling world without supervision, they seek ICA by comparing 2 sensory measurements (x1(t), x2(t))T equalsV X(t). Assuming a linear and instantaneous mixture model of the external world X(t) equals [A] S(t), where both the mixing matrix ([A] equalsV [a1, a2] of ICA vectors and the source percentages (s1(t), s2(t))T equalsV S(t) are unknown, we seek the independent sources approximately equals [I] where the approximated sign indicates that higher order statistics (HOS) may not be trivial. Without a teacher, the ANN weight matrix [W] equalsV [w1, w2] adjusts the outputs V(t) equals tanh([W]X(t)) approximately equals [W]X(t) until no desired outputs except the (Gaussian) 'garbage' (neither YES '1' nor NO '-1' but at linear may-be range 'origin 0') defined by Gaussian covariance G equals [I] equals [W][A] the internal knowledge representation [W], as the inverse of the external world matrix [A]-1. To unify IC, PCA, ANN & HOS theories since 1991 (advanced by Jutten & Herault, Comon, Oja, Bell-Sejnowski, Amari-Cichocki, Cardoso), the LYAPONOV function L(v1,...,vn, w1,...wn,) equals E(v1,...,vn) - H(w1,...wn) is constructed as the HELMHOTZ free energy to prove both convergences of supervised energy E and unsupervised entropy H learning. Consequently, rather using the faithful but dumb computer: 'GARBAGE-IN, GARBAGE-OUT,' the smarter neurocomputer will be equipped with an unsupervised learning that extracts

  20. Agglomerative concentric hypersphere clustering applied to structural damage detection

    Science.gov (United States)

    Silva, Moisés; Santos, Adam; Santos, Reginaldo; Figueiredo, Eloi; Sales, Claudomiro; Costa, João C. W. A.

    2017-08-01

    The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures. Continuous structural monitoring systems often require unsupervised approaches to automatically infer the health condition of a structure. However, when a structure is under linear and nonlinear effects caused by environmental and operational variability, data normalization procedures are also required to overcome these effects. The proposed approach aims, through a straightforward clustering procedure, to discover automatically the optimal number of clusters, representing the main state conditions of a structural system. Three initialization procedures are introduced to evaluate the impact of deterministic and stochastic initializations on the performance of this approach. The ACH is compared to state-of-the-art approaches, based on Gaussian mixture models and Mahalanobis squared distance, on standard data sets from a post-tensioned bridge located in Switzerland: the Z-24 Bridge. The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters. Furthermore, it reveals a better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, demonstrating a promising applicability in real-world structural health monitoring scenarios.

  1. Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means

    Science.gov (United States)

    Yangmin, GUO; Yun, TANG; Yu, DU; Shisong, TANG; Lianbo, GUO; Xiangyou, LI; Yongfeng, LU; Xiaoyan, ZENG

    2018-06-01

    Laser-induced breakdown spectroscopy (LIBS) combined with K-means algorithm was employed to automatically differentiate industrial polymers under atmospheric conditions. The unsupervised learning algorithm K-means were utilized for the clustering of LIBS dataset measured from twenty kinds of industrial polymers. To prevent the interference from metallic elements, three atomic emission lines (C I 247.86 nm , H I 656.3 nm, and O I 777.3 nm) and one molecular line C–N (0, 0) 388.3 nm were used. The cluster analysis results were obtained through an iterative process. The Davies–Bouldin index was employed to determine the initial number of clusters. The average relative standard deviation values of characteristic spectral lines were used as the iterative criterion. With the proposed approach, the classification accuracy for twenty kinds of industrial polymers achieved 99.6%. The results demonstrated that this approach has great potential for industrial polymers recycling by LIBS.

  2. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification.

    Science.gov (United States)

    Ye, Qing; Pan, Hao; Liu, Changhua

    2015-01-01

    A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

  3. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

    Directory of Open Access Journals (Sweden)

    Qing Ye

    2015-01-01

    Full Text Available A novel semisupervised extreme learning machine (ELM with clustering discrimination manifold regularization (CDMR framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE. The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

  4. Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

    Directory of Open Access Journals (Sweden)

    Martinez Fernando J

    2010-03-01

    Full Text Available Abstract Background Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1 post-bronchodilator FEV1 percent predicted, 2 percent bronchodilator responsiveness, and quantitative CT measurements of 3 apical emphysema and 4 airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1 emphysema predominant, 2 bronchodilator responsive, with higher FEV1; 3 discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4 airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant was associated with TGFB1 SNP rs1800470. Conclusions Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

  5. Sensitization trajectories in childhood revealed by using a cluster analysis

    DEFF Research Database (Denmark)

    Schoos, Ann-Marie M.; Chawes, Bo L.; Melen, Erik

    2017-01-01

    Prospective Studies on Asthma in Childhood 2000 (COPSAC2000) birth cohort with specific IgE against 13 common food and inhalant allergens at the ages of ½, 1½, 4, and 6 years. An unsupervised cluster analysis for 3-dimensional data (nonnegative sparse parallel factor analysis) was used to extract latent......BACKGROUND: Assessment of sensitization at a single time point during childhood provides limited clinical information. We hypothesized that sensitization develops as specific patterns with respect to age at debut, development over time, and involved allergens and that such patterns might be more...... biologically and clinically relevant. OBJECTIVE: We sought to explore latent patterns of sensitization during the first 6 years of life and investigate whether such patterns associate with the development of asthma, rhinitis, and eczema. METHODS: We investigated 398 children from the at-risk Copenhagen...

  6. Clustering by Partitioning around Medoids using Distance-Based ...

    African Journals Online (AJOL)

    OLUWASOGO

    outperforms both the Euclidean and Manhattan distance metrics in certain situations. KEYWORDS: PAM ... version of a dataset, compare the quality of clusters obtained from the Euclidean .... B. Theoretical Framework and Methodology.

  7. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    Science.gov (United States)

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  8. Diversity among galaxy clusters

    International Nuclear Information System (INIS)

    Struble, M.F.; Rood, H.J.

    1988-01-01

    The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

  9. Unsupervised exercise in survivors of human papillomavirus related head and neck cancer: how many can go it alone?

    Science.gov (United States)

    Bauml, Joshua; Kim, Jiyoung; Zhang, Xiaochen; Aggarwal, Charu; Cohen, Roger B; Schmitz, Kathryn

    2017-08-01

    Patients with human papillomavirus (HPV)-related head and neck cancer (HNC) have a better prognosis relative to other types of HNC, making survivorship an emerging and critical issue. Exercise is a core component of survivorship care, but little is known about how many survivors of HPV-related HNC can safely be advised to start exercising on their own, as opposed to needing further evaluation or supervised exercise. We utilized guidelines to identify health issues that would indicate value of further evaluation prior to being safely prescribed unsupervised exercise. We performed a retrospective chart review of 150 patients with HPV-related HNC to assess health issues 6 months after completing definitive therapy. Patients with at least one health issue were deemed appropriate to receive further evaluation prior to prescription for unsupervised exercise. We utilized logistic regression to identify clinical and demographic factors associated with the need for further evaluation, likely performed by outpatient rehabilitation clinicians. In this cohort of patients, 39.3% could safely be prescribed unsupervised exercise 6 months after completing definitive therapy. On multivariable regression, older age, BMI >30, and receipt of radiation were associated with an increased likelihood for requiring further evaluation or supervised exercise. Over half of patients with HPV-related HNC would benefit from referral to physical therapy or an exercise professional for further evaluation to determine the most appropriate level of exercise supervision, based upon current guidelines. Development of such referral systems will be essential to enhance survivorship outcomes for patients who have completed treatment.

  10. Unsupervised Tattoo Segmentation Combining Bottom-Up and Top-Down Cues

    Energy Technology Data Exchange (ETDEWEB)

    Allen, Josef D [ORNL

    2011-01-01

    Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for nding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a gure-ground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is e cient and suitable for further tattoo classi cation and retrieval purpose.

  11. A low-cost single-board solution for real-time, unsupervised waveform classification of multineuron recordings.

    Science.gov (United States)

    Kreiter, A K; Aertsen, A M; Gerstein, G L

    1989-10-01

    We describe a low-cost single-board system for unsupervised, real-time spike sorting of recordings from a number of neurons on a single microelectrode. The maximum number of spike classes depends on the quality of the recording; it will typically be between 2 and 5. The spike sorter communicates with a conventional microcomputer through a standard serial port (RS232). For typical firing rates as measured in the mammalian central nervous system, this set-up will accommodate up to some 10 parallel spike sorters for as many separate microelectrodes.

  12. Unsupervised motion-compensation of multi-slice cardiac perfusion MRI

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Ólafsdóttir, Hildur; Larsson, Henrik B. W.

    2005-01-01

    This paper presents a novel method for registration of single and multi-slice cardiac perfusion MRI. Utilising computer intensive analyses of variance and clustering in an annotated training set off-line, the presented method is capable of providing registration without any manual interaction...... in less than a second per frame. Changes in image intensity during the bolus passage are modelled by a slice-coupled active appearance model, which is augmented with a cluster analysis of the training set. Landmark correspondences are optimised using the MDL framework due to Davies et al. Image search...

  13. Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

    Science.gov (United States)

    Muraoka, Masae; Okuda, Hiroshi

    With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

  14. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

    2017-01-01

    The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

  15. Experience with a clustered parallel reduction machine

    NARCIS (Netherlands)

    Beemster, M.; Hartel, Pieter H.; Hertzberger, L.O.; Hofman, R.F.H.; Langendoen, K.G.; Li, L.L.; Milikowski, R.; Vree, W.G.; Barendregt, H.P.; Mulder, J.C.

    A clustered architecture has been designed to exploit divide and conquer parallelism in functional programs. The programming methodology developed for the machine is based on explicit annotations and program transformations. It has been successfully applied to a number of algorithms resulting in a

  16. What Makes Clusters Decline?

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    2015-01-01

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark. The longit...... but being quick to withdraw in times of crisis....

  17. Clustering of correlated networks

    OpenAIRE

    Dorogovtsev, S. N.

    2003-01-01

    We obtain the clustering coefficient, the degree-dependent local clustering, and the mean clustering of networks with arbitrary correlations between the degrees of the nearest-neighbor vertices. The resulting formulas allow one to determine the nature of the clustering of a network.

  18. Unsupervised Change Detection for Geological and Ecological Monitoring via Remote Sensing: Application on a Volcanic Area

    Science.gov (United States)

    Falco, N.; Pedersen, G. B. M.; Vilmunandardóttir, O. K.; Belart, J. M. M. C.; Sigurmundsson, F. S.; Benediktsson, J. A.

    2016-12-01

    The project "Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS)" aims at providing fast and reliable mapping and monitoring techniques on a big spatial scale with a high temporal resolution of the Icelandic landscape. Such mapping and monitoring will be crucial to both mitigate and understand the scale of processes and their often complex interlinked feedback mechanisms.In the EMMIRS project, the Hekla volcano area is one of the main sites under study, where the volcanic eruptions, extreme weather and human activities had an extensive impact on the landscape degradation. The development of innovative remote sensing approaches to compute earth observation variables as automatically as possible is one of the main tasks of the EMMIRS project. Furthermore, a temporal remote sensing archive is created and composed by images acquired by different sensors (Landsat, RapidEye, ASTER and SPOT5). Moreover, historical aerial stereo photos allowed decadal reconstruction of the landscape by reconstruction of digital elevation models. Here, we propose a novel architecture for automatic unsupervised change detection analysis able to ingest multi-source data in order to detect landscape changes in the Hekla area. The change detection analysis is based on multi-scale analysis, which allows the identification of changes at different level of abstraction, from pixel-level to region-level. For this purpose, operators defined in mathematical morphology framework are implemented to model the contextual information, represented by the neighbour system of a pixel, allowing the identification of changes related to both geometrical and spectral domains. Automatic radiometric normalization strategy is also implemented as pre-processing step, aiming at minimizing the effect of different acquisition conditions. The proposed architecture is tested on multi-temporal data sets acquired over different time periods coinciding with the last three eruptions (1980-1981, 1991

  19. Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.

    Science.gov (United States)

    Faisan, Sylvain; Thoraval, Laurent; Armspach, Jean-Paul; Foucher, Jack R; Metz-Lutz, Marie-Noëlle; Heitz, Fabrice

    2005-01-01

    activation patterns thanks to the unsupervised character of the HSMESM mapping approach. Along with activation maps, the method offers a wide range of additional fMRI analysis functionalities, including activation lag mapping, activation mode visualization, and hemodynamic response function analysis. Real event-related data: Activation detection results confirm and validate the overall strategy that consists in focusing the analysis on the transients, time-localized events that are the HROs. All the experiments performed on synthetic and real fMRI data demonstrate the relevance of HSMESMs in fMRI brain mapping. In particular, the statistical character of these models, along with their learning and generalizing abilities are of particular interest when dealing with strong variabilities of the active fMRI signal across time, space, experiments, and subjects.

  20. Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization.

    Science.gov (United States)

    Cai, Yun; Gu, Hong; Kenney, Toby

    2017-08-31

    Learning the structure of microbial communities is critical in understanding the different community structures and functions of microbes in distinct individuals. We view microbial communities as consisting of many subcommunities which are formed by certain groups of microbes functionally dependent on each other. The focus of this paper is on methods for extracting the subcommunities from the data, in particular Non-Negative Matrix Factorization (NMF). Our methods can be applied to both OTU data and functional metagenomic data. We apply the existing unsupervised NMF method and also develop a new supervised NMF method for extracting interpretable information from classification problems. The relevance of the subcommunities identified by NMF is demonstrated by their excellent performance for classification. Through three data examples, we demonstrate how to interpret the features identified by NMF to draw meaningful biological conclusions and discover hitherto unidentified patterns in the data. Comparing whole metagenomes of various mammals, (Muegge et al., Science 332:970-974, 2011), the biosynthesis of macrolides pathway is found in hindgut-fermenting herbivores, but not carnivores. This is consistent with results in veterinary science that macrolides should not be given to non-ruminant herbivores. For time series microbiome data from various body sites (Caporaso et al., Genome Biol 12:50, 2011), a shift in the microbial communities is identified for one individual. The shift occurs at around the same time in the tongue and gut microbiomes, indicating that the shift is a genuine biological trait, rather than an artefact of the method. For whole metagenome data from IBD patients and healthy controls (Qin et al., Nature 464:59-65, 2010), we identify differences in a number of pathways (some known, others new). NMF is a powerful tool for identifying the key features of microbial communities. These identified features can not only be used to perform difficult