Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1630
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.contributor.author | Kosmopoulos, Dimitrios I. | - |
dc.contributor.author | Varvarigou, Theodora | - |
dc.date.accessioned | 2013-02-20T13:48:20Z | en |
dc.date.accessioned | 2013-05-17T05:22:37Z | - |
dc.date.accessioned | 2015-12-02T10:02:26Z | - |
dc.date.available | 2013-02-20T13:48:20Z | en |
dc.date.available | 2013-05-17T05:22:37Z | - |
dc.date.available | 2015-12-02T10:02:26Z | - |
dc.date.issued | 2008-03 | - |
dc.identifier.citation | IEEE transactions on signal processing, 2008, vol. 56, iss. 3, pp. 949-963 | en_US |
dc.identifier.issn | 19410476 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/1630 | - |
dc.description.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 | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Transactions on Signal Processing | en_US |
dc.rights | © IEEE | en_US |
dc.subject | Signal processing | en_US |
dc.subject | Classification | en_US |
dc.subject | Expectation-maximization algorithm | en_US |
dc.subject | Cluster analysis | en_US |
dc.title | Signal modeling and classification using a robust latent space model based on t distributions | en_US |
dc.type | Article | en_US |
dc.collaboration | National Technical University Of Athens | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.journals | Subscription | en_US |
dc.country | Greece | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1109/TSP.2007.907912 | en_US |
dc.dept.handle | 123456789/54 | en |
dc.relation.issue | 3 | en_US |
dc.relation.volume | 56 | en_US |
cut.common.academicyear | 2007-2008 | en_US |
dc.identifier.spage | 949 | en_US |
dc.identifier.epage | 963 | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.grantfulltext | none | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4956-4013 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
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