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dc.contributor.authorSharma, Alok
dc.contributor.authorPahwal, Kuldip K
dc.date.accessioned2017-05-03T13:01:10Z
dc.date.available2017-05-03T13:01:10Z
dc.date.issued2007
dc.date.modified2009-09-21T05:51:11Z
dc.identifier.issn0167-8655
dc.identifier.doi10.1016/j.patrec.2007.01.012
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/505619/description#description
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1151
dc.relation.ispartofpageto1155
dc.relation.ispartofjournalPattern Recognition Letters
dc.relation.ispartofvolume28
dc.rights.retentionY
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode5204
dc.titleFast principal component analysis using fixed-point algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.date.issued2007
gro.hasfulltextNo Full Text
gro.griffith.authorPaliwal, Kuldip K.


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