Show simple item record

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.modified2009-05-19T06:41:45Z
dc.identifier.issn15493636en_US
dc.identifier.urihttp://hdl.handle.net/10072/14348
dc.description.abstractLinear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotates the classes individually in the original feature space in a manner that enables further reduction of error. In this paper we present an extension of the rotational LDA technique by utilizing Bayes decision theory for class separation which improves the classification performance even further.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherScience Publicationsen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://www.scipub.org/fulltext/jcs/jcs29754-757.pdfen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom754en_US
dc.relation.ispartofpageto757en_US
dc.relation.ispartofissue9en_US
dc.relation.ispartofjournalJournal of Computer Scienceen_US
dc.relation.ispartofvolume2en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleRotational Linear Discriminant Analysis Using Bayes Rule 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.date.issued2006
gro.hasfulltextNo Full Text


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record