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Title: Signal modeling and classification using a robust latent space model based on t distributions
Authors: Chatzis, Sotirios P. 
Kosmopoulos, Dimitrios 
Varvarigou, Theodora 
Chatzis, Sotirios P. 
Kosmopoulos, Dimitrios 
Varvarigou, Theodora 
Keywords: Signal processing;Classification;Expectation-maximization algorithm;Cluster analysis
Issue Date: 2008
Publisher: IEEE Xplore
Source: IEEE transactions on signal processing, 2008, Volume 56, Issue 3, Pages 949-963
Abstract: Factor analysis is a statistical covariance modeling technique based on the assumption of normally distributed data. A mixture of factor analyzers can be hence viewed as a special case of Gaussian (normal) mixture models providing a mathematically sound framework for attribute space dimensionality reduction. A significant shortcoming of mixtures of factor analyzers is the vulnerability of normal distributions to outliers. Recently, the replacement of normal distributions with the heavier-tailed Student's-t distributions has been proposed as a way to mitigate these shortcomings and the treatment of the resulting model under an expectation-maximization (EM) algorithm framework has been conducted. In this paper, we develop a Bayesian approach to factor analysis modeling based on Student's-t distributions. We derive a tractable variational inference algorithm for this model by expressing the Student's-t distributed factor analyzers as a marginalization over additional latent variables. Our innovative approach provides an efficient and more robust alternative to EM-based methods, resolving their singularity and overfitting proneness problems, while allowing for the automatic determination of the optimal model size. We demonstrate the superiority of the proposed model over well-known covariance modeling techniques in a wide range of signal processing applications
ISSN: 1053-587X
DOI: 10.1109/TSP.2007.907912
Rights: © 2008 IEEE
Type: Article
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