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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