Ranking methods for tensor components analysis and their application to face images

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dc.contributor.authorFilisbino T. A.
dc.contributor.authorGiraldi G. A.
dc.contributor.authorCarlos E. Thomaz
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2022-01-12T22:01:17Z
dc.date.available2022-01-12T22:01:17Z
dc.date.issued2013-08-05
dc.description.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 hyper planes, 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. © 2013 IEEE.
dc.description.firstpage312
dc.description.lastpage319
dc.identifier.citationFILISBINO, T. A.; GIRALDI, G. A.; THOMAZ, C. E. Ranking methods for tensor components analysis and their application to face images. Brazilian Symposium of Computer Graphic and Image Processing, p. 312-319, 2013.
dc.identifier.doi10.1109/SIBGRAPI.2013.50
dc.identifier.issn1530-1834
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4057
dc.relation.ispartofBrazilian Symposium of Computer Graphic and Image Processing
dc.rightsAcesso Restrito
dc.subject.otherlanguageCSA
dc.subject.otherlanguageDimensionality Reduction
dc.subject.otherlanguageFace Image Analysis
dc.subject.otherlanguageTensor Subspace Learning
dc.titleRanking methods for tensor components analysis and their application to face images
dc.typeArtigo de evento
fei.scopus.citations4
fei.scopus.eid2-s2.0-84891527329
fei.scopus.subjectCovariance structures
fei.scopus.subjectCSA
fei.scopus.subjectData representations
fei.scopus.subjectDimensionality reduction
fei.scopus.subjectFace image analysis
fei.scopus.subjectGender classification
fei.scopus.subjectMulti-dimensional images
fei.scopus.subjectTensor subspace learning
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84891527329&origin=inward
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