Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17375
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorYang, J-
dc.creatorZhang, D-
dc.creatorYang, JY-
dc.date.accessioned2014-12-31T08:01:57Z-
dc.date.available2014-12-31T08:01:57Z-
dc.identifier.issn1433-7541 (print)en_US
dc.identifier.issn1433-755X (online)en_US
dc.identifier.urihttp://hdl.handle.net/10397/17375-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectHigh dimensional problemen_US
dc.subjectK-L expansionen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectSmall sample size problemen_US
dc.titleA generalised K-L expansion method which can deal with small sample size and high-dimensional problemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage47en_US
dc.identifier.epage54en_US
dc.identifier.volume6en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1007/s10044-002-0177-3en_US
dcterms.abstractThe K-L expansion method, which is able to extract the discriminatory information contained in class-mean vectors, is generalised, in this paper, to make it suitable for solving small sample size problems. We further investigate, theoretically, how to reduce the method's computational complexity in high-dimensional cases. As a result, a simple and efficient GKLE algorithm is developed. We test our method on the ORL face image database and the NUST603 handwritten Chinese character database, and our experimental results demonstrate that GKLE outperforms the existing techniques of PCA, PCA plus LDA, and Direct LDA.-
dcterms.bibliographicCitationPattern analysis and applications, 2003, v. 6, no. 1, p. 47-54-
dcterms.isPartOfPattern analysis and applications-
dcterms.issued2003-
dc.identifier.isiWOS:000183382700006-
dc.identifier.scopus2-s2.0-0038472104-
dc.identifier.rosgroupidr12551-
dc.description.ros2002-2003 > Academic research: refereed > Publication in refereed journal-
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