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dc.contributor.authorSharma, Aloken_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.date.accessioned2017-05-03T13:01:07Z
dc.date.available2017-05-03T13:01:07Z
dc.date.issued2006en_US
dc.date.modified2009-09-21T05:48:34Z
dc.identifier.issn00313203en_US
dc.identifier.doi10.1016/j.patcog.2006.04.021en_AU
dc.identifier.urihttp://hdl.handle.net/10072/14346
dc.description.abstractThis discussion presents a new perspective of subspace independent component analysis (ICA). The notion of a function of cumulants (kurtosis) is generalized to vector kurtosis. This vector kurtosis is utilized in the subspace ICA algorithm to estimate subspace independent components. One of the main advantages of the presented approach is its computational simplicity. The experiments have shown promising results in estimating subspace independent components.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherPergamonen_US
dc.publisher.placeUnited Kingdomen_US
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#descriptionen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom2227en_US
dc.relation.ispartofpageto2232en_US
dc.relation.ispartofjournalPattern Recognitionen_US
dc.relation.ispartofvolume39en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleSubspace independent component analysis using vector kurtosisen_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.issued2006
gro.hasfulltextNo Full Text


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