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Article: Block principal component analysis for tensor objects with frequency or time information

TitleBlock principal component analysis for tensor objects with frequency or time information
Authors
KeywordsTensors
Gait recognition
Feature extraction
Covariance matrix
Block matrix
Hyperspectral face recognition
Issue Date2018
Citation
Neurocomputing, 2018, v. 302, p. 12-22 How to Cite?
Abstract© 2018 Feature extraction is a prerequisite in many machine learning and data mining applications. As the advancement of data acquisition techniques, nowadays tensor objects are accumulated with respect to frequency or time information in a great number of fields. For instance color or hyperspectral faces in multichannel information, and human gait motion in time information are obtained. In this paper, we propose and develop a block principal component analysis (BPCA) to extract features for this kind of tensor objects. Our idea is to unfold tensor objects according to their spatial information and frequency/time information, and represent them in block matrix form. The corresponding covariance matrix for frequency/time information can be captured and used. The block eigen-decomposition of such covariance matrix is employed to seek for projection solution as features. Both reconstruction and classification problems can be solved via these projected features. Extensive experiments have been conducted on various face or gait databases to demonstrate the superiority of BPCA compared with existing methods such as PCA, (2D)2PCA, MPCA, and UMPCA in terms of effectiveness. Moreover, the proposed BPCA is competitively efficient compared to these existing methods.
Persistent Identifierhttp://hdl.handle.net/10722/277087
ISSN
2017 Impact Factor: 3.241
2015 SCImago Journal Rankings: 1.202

 

DC FieldValueLanguage
dc.contributor.authorLi, Xutao-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorXu, Xiaofei-
dc.contributor.authorYe, Yunming-
dc.date.accessioned2019-09-18T08:35:34Z-
dc.date.available2019-09-18T08:35:34Z-
dc.date.issued2018-
dc.identifier.citationNeurocomputing, 2018, v. 302, p. 12-22-
dc.identifier.issn0925-2312-
dc.identifier.urihttp://hdl.handle.net/10722/277087-
dc.description.abstract© 2018 Feature extraction is a prerequisite in many machine learning and data mining applications. As the advancement of data acquisition techniques, nowadays tensor objects are accumulated with respect to frequency or time information in a great number of fields. For instance color or hyperspectral faces in multichannel information, and human gait motion in time information are obtained. In this paper, we propose and develop a block principal component analysis (BPCA) to extract features for this kind of tensor objects. Our idea is to unfold tensor objects according to their spatial information and frequency/time information, and represent them in block matrix form. The corresponding covariance matrix for frequency/time information can be captured and used. The block eigen-decomposition of such covariance matrix is employed to seek for projection solution as features. Both reconstruction and classification problems can be solved via these projected features. Extensive experiments have been conducted on various face or gait databases to demonstrate the superiority of BPCA compared with existing methods such as PCA, (2D)2PCA, MPCA, and UMPCA in terms of effectiveness. Moreover, the proposed BPCA is competitively efficient compared to these existing methods.-
dc.languageeng-
dc.relation.ispartofNeurocomputing-
dc.subjectTensors-
dc.subjectGait recognition-
dc.subjectFeature extraction-
dc.subjectCovariance matrix-
dc.subjectBlock matrix-
dc.subjectHyperspectral face recognition-
dc.titleBlock principal component analysis for tensor objects with frequency or time information-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.neucom.2018.02.014-
dc.identifier.scopuseid_2-s2.0-85046641540-
dc.identifier.volume302-
dc.identifier.spage12-
dc.identifier.epage22-
dc.identifier.eissn1872-8286-

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