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dc.contributor.authorSharma, Alok
dc.contributor.authorPaliwal, Kuldip
dc.date.accessioned2017-05-03T13:01:07Z
dc.date.available2017-05-03T13:01:07Z
dc.date.issued2006
dc.date.modified2009-05-19T06:41:45Z
dc.identifier.issn15493636
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherScience Publications
dc.publisher.placeUnited States
dc.publisher.urihttps://thescipub.com/jcs/issue/259
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom754
dc.relation.ispartofpageto757
dc.relation.ispartofissue9
dc.relation.ispartofjournalJournal of Computer Science
dc.relation.ispartofvolume2
dc.rights.retentionY
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode08
dc.titleRotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.date.issued2006
gro.hasfulltextNo Full Text
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorSharma, Alok


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