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dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.authorSharma, Aloken_US
dc.contributor.editorFarokh B Bastani, Steve McConnell (Editor-in-Chief)en_US
dc.date.accessioned2017-04-04T16:59:57Z
dc.date.available2017-04-04T16:59:57Z
dc.date.issued2008en_US
dc.date.modified2009-05-15T09:07:57Z
dc.identifier.issn1041-4347en_US
dc.identifier.doi10.1109/TKDE.2008.101en_AU
dc.identifier.urihttp://hdl.handle.net/10072/23591
dc.description.abstractThe linear discriminant analysis (LDA) technique is very popular in pattern recognition for dimensionality reduction. It is a supervised learning technique that finds a linear transformation such that the overlap between the classes is minimum for the projected feature vectors in the reduced feature space. This overlap, if present, adversely affects the classification performance. In this paper, we introduce prior to dimensionality-reduction transformation an additional rotational transform that rotates the feature vectors in the original feature space around their respective class centroids in such a way that the overlap between the classes in the reduced feature space is further minimized. As a result, the classification performance significantly improves, which is demonstrated using several data corpuses.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent3380011 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1336en_US
dc.relation.ispartofpageto1347en_US
dc.relation.ispartofissue10en_AU
dc.relation.ispartofjournalIEEE Transactions on Knowledge and Data Engineeringen_US
dc.relation.ispartofvolume20en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titleRotational Linear Discriminant Analysis Technique for Dimensionality Reductionen_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.rights.copyrightCopyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_AU
gro.date.issued2008
gro.hasfulltextFull Text


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