Ranking Tensor Subspaces in Weighted Multilinear Principal Component Analysis

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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.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2022-01-12T21:58:04Z
dc.date.available2022-01-12T21:58:04Z
dc.date.issued2017-07-01
dc.description.abstract© 2017 World Scientific Publishing Company.Multilinear principal component analysis (MPCA) has been applied for tensor decomposition and dimensionality reduction in image databases modeled through higher order tensors. Despite the well-known attractive properties of MPCA, the traditional approach does not incorporate prior information in order to steer its subspace computation. In this paper, we propose a method to explicitly incorporate such semantics in the MPCA framework to allow an automatic selective treatment of the variables that compose the patterns of interest. The method relies on spatial weights calculated, in this work, by separating hyperplanes and Fisher criterion. In this way, we can perform feature extraction and dimensionality reduction taking advantage of high level information in the form of labeled data. Besides, the corresponding tensor components are ranked in order to identify the principal weighted tensor subspaces for classification tasks. In the computational results we consider gender and facial expression experiments to illustrate the capabilities of the method for dimensionality reduction, classification and reconstruction of face images.
dc.description.issuenumber7
dc.description.volume31
dc.identifier.citationFILISBINO, T. A.; GIRALDI, G. A.; THOMAZ, C. E. Ranking Tensor Subspaces in Weighted Multilinear Principal Component Analysis. International Journal of Pattern Recognition and Artificial Intelligence, v. 31, n. 7, Jul, 2017.
dc.identifier.doi10.1142/S021800141751003X
dc.identifier.issn0218-0014
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3837
dc.relation.ispartofInternational Journal of Pattern Recognition and Artificial Intelligence
dc.rightsAcesso Restrito
dc.subject.otherlanguageMPCA
dc.subject.otherlanguageMultilinear subspace learning
dc.subject.otherlanguageranking tensor components
dc.subject.otherlanguageweighted multilinear principal component analysis
dc.titleRanking Tensor Subspaces in Weighted Multilinear Principal Component Analysis
dc.typeArtigo
fei.scopus.citations5
fei.scopus.eid2-s2.0-85017401751
fei.scopus.subjectDimensionality reduction
fei.scopus.subjectHigh-level information
fei.scopus.subjectMPCA
fei.scopus.subjectMultilinear principal component analysis (MPCA)
fei.scopus.subjectSeparating hyperplane
fei.scopus.subjectSubspace learning
fei.scopus.subjectTensor components
fei.scopus.subjectTraditional approaches
fei.scopus.updated2023-11-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85017401751&origin=inward
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