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dc.contributor.authorSharma, Aloken_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.date.accessioned2017-05-03T13:01:10Z
dc.date.available2017-05-03T13:01:10Z
dc.date.issued2007en_US
dc.date.modified2009-09-21T05:51:11Z
dc.identifier.issn01678655en_US
dc.identifier.doi10.1016/j.patrec.2007.01.012en_AU
dc.identifier.urihttp://hdl.handle.net/10072/18520
dc.description.abstractIn this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevier BVen_US
dc.publisher.placeNetherlandsen_US
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/505619/description#descriptionen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1151en_US
dc.relation.ispartofpageto1155en_US
dc.relation.ispartofjournalPattern Recognition Lettersen_US
dc.relation.ispartofvolume28en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleFast principal component analysis using fixed-point algorithmen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2007
gro.hasfulltextNo Full Text


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