<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<identifier>8JMKD3MGPBW34M/3EE5MJ5</identifier>
		<repository>sid.inpe.br/sibgrapi/2013/07.08.14.20</repository>
		<metadatarepository>sid.inpe.br/sibgrapi/2013/07.08.14.20.39</metadatarepository>
		<site>sibgrapi.sid.inpe.br 802</site>
		<citationkey>FilisbinoGiraThom:2013:RaMeTe</citationkey>
		<author>Filisbino, Tiene Andre,</author>
		<author>Giraldi, Antonio Giraldi,</author>
		<author>Thomaz, Carlos Eduardo,</author>
		<affiliation>National Laboratory for Scientific Computing</affiliation>
		<affiliation>National Laboratory for Scientific Computing</affiliation>
		<affiliation>Department of Electrical Engineering FEI</affiliation>
		<title>Ranking Methods for Tensor Components Analysis and their Application to Face Images</title>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<year>2013</year>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Aug. 5-8, 2013</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<keywords>Dimensionality Reduction, Tensor Subspace Learning, CSA, Face Image Analysis.</keywords>
		<abstract>Higher order tensors have been applied to model multidimensional image databases for subsequent tensor decomposition and dimensionality reduction. In this paper we address the problem of ranking tensor components in the context of the concurrent subspace analysis (CSA) technique following two distinct approaches: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes, to select the most discriminant CSA tensor components. The former follows a ranking method based on the covariance structure of each subspace in the CSA framework while the latter addresses the problem through the discriminant principal component analysis methodology. Both approaches are applied and compared in a gender classification task performed using the FEI face database. Our experimental results highlight the low dimensional data representation of both approaches, while allowing robust discriminant reconstruction and interpretation of the sample groups and high recognition rates.</abstract>
		<language>en</language>
		<tertiarytype>Full Paper</tertiarytype>
		<format>On-line.</format>
		<size>2935 KiB</size>
		<numberoffiles>1</numberoffiles>
		<targetfile>Sibgrapi_2013.pdf</targetfile>
		<lastupdate>2013:07.08.14.20.39 sid.inpe.br/banon/2001/03.30.15.38 tiene@lncc.br</lastupdate>
		<metadatalastupdate>2020:02.19.03.09.22 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2013}</metadatalastupdate>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<e-mailaddress>tiene@lncc.br</e-mailaddress>
		<usergroup>tiene@lncc.br</usergroup>
		<visibility>shown</visibility>
		<transferableflag>1</transferableflag>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<contenttype>External Contribution</contenttype>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2013/07.08.14.20</url>
	</metadata>
</metadatalist>