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dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.authorSharma, Aloken_US
dc.date.accessioned2017-05-03T14:51:31Z
dc.date.available2017-05-03T14:51:31Z
dc.date.issued2010en_US
dc.date.modified2011-03-29T06:53:58Z
dc.identifier.issn01678655en_US
dc.identifier.doi10.1016/j.patrec.2010.08.003en_AU
dc.identifier.urihttp://hdl.handle.net/10072/37823
dc.description.abstractThe direct linear discriminant analysis (DLDA) technique is a well known technique for dimensionality reduction. It can overcome the small sample size problem. However, its performance is limited. In this paper we address its drawbacks and propose an improvement of the DLDA technique. The experiment is conducted on several DNA microarray gene expression datasets and the performance (in terms of classification accuracy) of the proposed improvement of the technique is reported at 91.1% which is very promising.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevieren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom2489en_US
dc.relation.ispartofpageto2492en_US
dc.relation.ispartofissue16en_US
dc.relation.ispartofjournalPattern Recognition Lettersen_US
dc.relation.ispartofvolume31en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleImproved direct LDA and its application to DNA microarray gene expression dataen_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.issued2010
gro.hasfulltextNo Full Text


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