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|Title:||Signal modeling and classification using a robust latent space model based on t distributions||Authors:||Chatzis, Sotirios P.
Chatzis, Sotirios P.
|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||URI:||http://ktisis.cut.ac.cy/handle/10488/7296||ISSN:||1053-587X||DOI:||10.1109/TSP.2007.907912||Rights:||© 2008 IEEE||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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