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dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.authorSharma, Aloken_US
dc.date.accessioned2017-04-24T10:19:35Z
dc.date.available2017-04-24T10:19:35Z
dc.date.issued2011en_US
dc.date.modified2012-06-26T00:52:39Z
dc.identifier.issn1558884Xen_US
dc.identifier.urihttp://hdl.handle.net/10072/45639
dc.description.abstractThe regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherJPRRen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://www.jprr.org/index.php/jprr/article/view/370en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom298en_US
dc.relation.ispartofpageto306en_US
dc.relation.ispartofjournalJournal of Pattern Recognition Researchen_US
dc.relation.ispartofvolumeXen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleApproximate LDA Technique for Dimensionality Reduction in the Small Sample Size Caseen_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.issued2011
gro.hasfulltextNo Full Text


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