A new ranking method for principal components analysis and its application to face image analysis

Página do item simplificado
dc.contributor.authorThomaz C.E.
dc.contributor.authorGiraldi G.A.
dc.date.accessioned2019-08-19T23:45:21Z
dc.date.available2019-08-19T23:45:21Z
dc.date.issued2010
dc.description.abstractIn this work, we investigate a new ranking method for principal component analysis (PCA). Instead of sorting the principal components in decreasing order of the corresponding eigenvalues, we propose the idea of using the discriminant weights given by separating hyperplanes to select among the principal components the most discriminant ones. The method is not restricted to any particular probability density function of the sample groups because it can be based on either a parametric or non-parametric separating hyperplane approach. In addition, the number of meaningful discriminant directions is not limited to the number of groups, providing additional information to understand group differences extracted from high-dimensional problems. To evaluate the discriminant principal components, separation tasks have been performed using face images and three different databases. Our experimental results have shown that the principal components selected by the separating hyperplanes allow robust reconstruction and interpretation of the data, as well as higher recognition rates using less linear features in situations where the differences between the sample groups are subtle and consequently most difficult for the standard and state-of-the-art PCA selection methods. © 2009 Elsevier B.V. All rights reserved.
dc.description.firstpage902
dc.description.issuenumber6
dc.description.lastpage913
dc.description.volume28
dc.identifier.citationTHOMAZ, C. E.; GIRALDI, G. A.. A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing, v. 28, n. 6, p. 902-913, 2010.
dc.identifier.doi10.1016/j.imavis.2009.11.005
dc.identifier.issn0262-8856
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/1230
dc.relation.ispartofImage and Vision Computing
dc.rightsAcesso Restrito
dc.subject.otherlanguageFace image analysis
dc.subject.otherlanguageHigh dimensional problems
dc.subject.otherlanguagePrincipal components analysis
dc.subject.otherlanguageSeparating hyperplanes
dc.titleA new ranking method for principal components analysis and its application to face image analysis
dc.typeArtigo
fei.scopus.citations450
fei.scopus.eid2-s2.0-77649336466
fei.scopus.subjectEigenvalues
fei.scopus.subjectFace image analysis
fei.scopus.subjectFace images
fei.scopus.subjectGroup differences
fei.scopus.subjectHigh-dimensional problems
fei.scopus.subjectLinear feature
fei.scopus.subjectNon-parametric
fei.scopus.subjectPrincipal Components
fei.scopus.subjectPrincipal components analysis
fei.scopus.subjectRanking methods
fei.scopus.subjectRecognition rates
fei.scopus.subjectRobust reconstruction
fei.scopus.subjectSelection methods
fei.scopus.subjectSeparating hyperplane
fei.scopus.updated2024-03-04
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77649336466&origin=inward
Arquivos
Coleções
Artigos