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dc.contributor.authorSharma, Alok
dc.contributor.authorPaliwal, Kuldip K
dc.date.accessioned2017-12-07T00:07:25Z
dc.date.available2017-12-07T00:07:25Z
dc.date.issued2015
dc.identifier.issn1868-8071
dc.identifier.doi10.1007/s13042-013-0226-9
dc.identifier.urihttp://hdl.handle.net/10072/101329
dc.description.abstractDimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which can overcome SSS problem. We have also highlighted some important datasets and software/packages.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom443
dc.relation.ispartofpageto454
dc.relation.ispartofissue3
dc.relation.ispartofjournalInternational Journal of Machine Learning and Cybernetics
dc.relation.ispartofvolume6
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode0801
dc.titleLinear discriminant analysis for the small sample size problem: An overview
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2015 Springer Berlin Heidelberg. This is an electronic version of an article published in International Journal of Machine Learning and Cybernetics, Vol 6 Issue 3, pages 443-454, 2015. International Journal of Machine Learning and Cybernetics is available online at: http://link.springer.com/ with the open URL of your article.
gro.hasfulltextFull Text
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorSharma, Alok


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