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dc.contributor.authorWang, XC
dc.contributor.authorPaliwal, KK
dc.contributor.editorS.Y. Kung
dc.date.accessioned2017-05-03T13:01:17Z
dc.date.available2017-05-03T13:01:17Z
dc.date.issued2002
dc.date.modified2010-07-28T07:00:37Z
dc.identifier.issn0922-5773
dc.identifier.doi10.1023/A:1016307200123
dc.identifier.urihttp://hdl.handle.net/10072/6642
dc.description.abstractDimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not all the newly added features are helpful to classification. Therefore it is necessary to reduce the dimensionality of feature space for effective and efficient pattern recognition. Two popular methods for dimensionality reduction are Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). While these methods are effective, there exists an inconsistency between feature extraction and the classification objective. In this paper we use Minimum Classification Error (MCE) training algorithm for feature dimensionality reduction and classification on Daterding and GLASS databases. The results of MCE training algorithms are compared with those of LDA and PCA.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherKluwer Academic Publishers
dc.publisher.placeBoston, USA
dc.relation.ispartofpagefrom19
dc.relation.ispartofpageto28
dc.relation.ispartofjournalJournal of VLSI Signal Processing Systems for Signal, Image and Video Technology
dc.relation.ispartofvolume32
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode0906
dc.titleA modified minimum classification error (MCE) training algorithm for dimensionality reduction
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.date.issued2002
gro.hasfulltextNo Full Text
gro.griffith.authorPaliwal, Kuldip K.


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