Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1630
Title: Signal modeling and classification using a robust latent space model based on t distributions
Authors: Chatzis, Sotirios P. 
Kosmopoulos, Dimitrios I. 
Varvarigou, Theodora 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Signal processing;Classification;Expectation-maximization algorithm;Cluster analysis
Issue Date: Mar-2008
Source: IEEE transactions on signal processing, 2008, vol. 56, iss. 3, pp. 949-963
Volume: 56
Issue: 3
Start page: 949
End page: 963
Journal: IEEE Transactions on Signal Processing 
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: https://hdl.handle.net/20.500.14279/1630
ISSN: 19410476
DOI: 10.1109/TSP.2007.907912
Rights: © IEEE
Type: Article
Affiliation : National Technical University Of Athens 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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