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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3EE5MJ5
Repositorysid.inpe.br/sibgrapi/2013/07.08.14.20
Last Update2013:07.08.14.20.39 tiene@lncc.br
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Metadata Last Update2020:02.19.03.09.22 administrator
Citation KeyFilisbinoGiraThom:2013:RaMeTe
TitleRanking Methods for Tensor Components Analysis and their Application to Face Images
FormatOn-line.
Year2013
DateAug. 5-8, 2013
Access Date2020, Dec. 04
Number of Files1
Size2935 KiB
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Author1 Filisbino, Tiene Andre
2 Giraldi, Antonio Giraldi
3 Thomaz, Carlos Eduardo
Affiliation1 National Laboratory for Scientific Computing
2 National Laboratory for Scientific Computing
3 Department of Electrical Engineering FEI
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addresstiene@lncc.br
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2013-07-08 14:20:39 :: tiene@lncc.br -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Document Stagecompleted
Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsDimensionality Reduction, Tensor Subspace Learning, CSA, Face Image Analysis.
AbstractHigher 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.
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