Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/191
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dc.contributorDepartment of Computing-
dc.creatorYang, J-
dc.creatorFrangi, AF-
dc.creatorYang, JY-
dc.creatorZhang, DD-
dc.creatorJin, Z-
dc.date.accessioned2014-12-11T08:22:59Z-
dc.date.available2014-12-11T08:22:59Z-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10397/191-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectKernel-based methodsen_US
dc.subjectSubspace methodsen_US
dc.subjectPrincipal component analysis (PCA)en_US
dc.subjectFisher linear discriminant analysis (LDA or FLD)en_US
dc.subjectFeature extractionen_US
dc.subjectMachine learningen_US
dc.subjectFace recognitionen_US
dc.subjectHandwritten digit recognitionen_US
dc.titleKPCA Plus LDA : a complete kernel Fisher discriminant framework for feature extraction and recognitionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage230-
dc.identifier.epage244-
dc.identifier.volume27-
dc.identifier.issue2-
dc.identifier.doi10.1109/TPAMI.2005.33-
dcterms.abstractThis paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in “double discriminant subspaces.” The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on pattern analysis and machine intelligence, Feb. 2005, v. 27, no. 2, p. 230-244-
dcterms.isPartOfIEEE transactions on pattern analysis and machine intelligence-
dcterms.issued2005-02-
dc.identifier.isiWOS:000225689300006-
dc.identifier.scopus2-s2.0-14544297033-
dc.identifier.eissn1939-3539-
dc.identifier.rosgroupidr22904-
dc.description.ros2004-2005 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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