Results 1  10
of
75
Independent Component Analysis
 Neural Computing Surveys
, 2001
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 1488 (93 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Wellknown linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA. 1
How many clusters? Which clustering method? Answers via modelbased cluster analysis
 THE COMPUTER JOURNAL
, 1998
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ModelBased Clustering, Discriminant Analysis, and Density Estimation
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
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Cited by 259 (24 self)
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Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as \How many clusters are there?", "Which clustering method should be used?" and \How should outliers be handled?". We outline a general methodology for modelbased clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, mineeld detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology, a...
MCLUST: Software for Modelbased Cluster Analysis
 Journal of Classification
, 1999
"... MCLUST is a software package for cluster analysis written in Fortran and interfaced to the SPLUS commercial software package1. It implements parameterized Gaussian hierarchical clustering algorithms [16, 1, 7] and the EM algorithm for parameterized Gaussian mixture models [5, 13, 3, 14] with the po ..."
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Cited by 52 (16 self)
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MCLUST is a software package for cluster analysis written in Fortran and interfaced to the SPLUS commercial software package1. It implements parameterized Gaussian hierarchical clustering algorithms [16, 1, 7] and the EM algorithm for parameterized Gaussian mixture models [5, 13, 3, 14] with the possible addition of a Poisson noise term. MCLUST also includes functions that combine hierarchical clustering, EM and the Bayesian Information Criterion (BIC) in a comprehensive clustering strategy [4, 8]. Methods of this type have shown promise in a number of practical applications, including character recognition [16], tissue segmentation [1], mine eld and seismic fault detection [4], identi cation of textile aws from images [2], and classi cation of astronomical data [3, 15]. Aweb page with related links can be found at
Computational Auditory Scene Recognition
 In IEEE Int’l Conf. on Acoustics, Speech, and Signal Processing
, 2001
"... v 1 ..."
Health status monitoring through analysis of behavioral patterns
 8th congress of the Italian Association for Artificial Intelligence (AI*IA) on Ambient Intelligence
, 2003
"... Abstract. With the rapid growth of the elderly population, there is a need to assess the ability of elders to maintain an independent and healthy lifestyle. One possible method is to employ the concepts of ambient intelligence to remotely monitor an elder’s activity. The SmartHouse project uses a sy ..."
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Cited by 36 (4 self)
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Abstract. With the rapid growth of the elderly population, there is a need to assess the ability of elders to maintain an independent and healthy lifestyle. One possible method is to employ the concepts of ambient intelligence to remotely monitor an elder’s activity. The SmartHouse project uses a system of basic sensors to monitor a person’s inhome activity, and a prototype of the system is being tested within a subject’s home. We examine whether the system can be used to detect behavioral patterns. Mixture models are used to develop a probabilistic model of behavioral patterns. The results of the mixture model analysis are then compared to a log of events kept by the user. 1
MCLUST: Software for ModelBased Cluster and Discriminant Analysis
, 1998
"...  k ) , (1) where x represents the data, and k is an integer subscript specifying a particular cluster. Clusters are ellipsoidal, centered at the means k . The covariances # k determine their other geometric features. # Funded by the O#ce of Naval Research under contracts N000149610192 an ..."
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Cited by 35 (1 self)
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 k ) , (1) where x represents the data, and k is an integer subscript specifying a particular cluster. Clusters are ellipsoidal, centered at the means k . The covariances # k determine their other geometric features. # Funded by the O#ce of Naval Research under contracts N000149610192 and N000149610330. 1 MathSoft, Inc., Seattle, WA USA  http://www.mathsoft.com/splus 2 see http://lib.stat.cmu.edu/R/CRAN 1 Each covariance matrix is parameterized by eigenvalue decomposition in the form # k = # k D k A k D T k , where D k is the orthogonal matrix of eigenvectors, A k is a diagonal matrix whose elements are proportional to the eigenvalues of # k , and # k is a scalar. The orie
An equivalence of the EM and ICE algorithm for exponential family
 IEEE Trans. Signal Processing
, 1997
"... Abstract—In this correspondence, we compare the expectation maximization (EM) algorithm with another iterative approach, namely, the iterative conditional estimation (ICE) algorithm, which was formally introduced in the field of statistical segmentation of images. We show that in case the probabilit ..."
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Cited by 33 (0 self)
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Abstract—In this correspondence, we compare the expectation maximization (EM) algorithm with another iterative approach, namely, the iterative conditional estimation (ICE) algorithm, which was formally introduced in the field of statistical segmentation of images. We show that in case the probability density function (PDF) belongs to the exponential family, the EM algorithm is one particular case of the ICE algorithm. I.
Image Denoising by Sparse Code Shrinkage
 Intelligent Signal Processing
, 1999
"... Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely signicantly active. Such a representation is closely related to independent component analysis (ICA), and has some neurophysiological plausibility. In this paper, we sh ..."
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Cited by 31 (2 self)
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Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely signicantly active. Such a representation is closely related to independent component analysis (ICA), and has some neurophysiological plausibility. In this paper, we show how sparse coding can be used for image denoising. We model the noisefree image data by independent component analysis, and denoise a noisy image by maximum likelihood estimation of the noisy version of the ICA model. This leads to the application of a softthresholding (shrinkage) operator on the components of sparse coding. Our method is closely related to the method of wavelet shrinkage and coring methods, but it has the important benefit that the representation is determined solely by the statistical properties of the data. In fact, our method can be seen as a simple rederivation of the wavelet shrinkage method for image data, using just the basic principle of maximum likelihoo...