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dc.contributor.authorXu, T.
dc.contributor.authorLu, C.
dc.contributor.authorLiu, Wan-quan
dc.contributor.editorLoi Lei Lai
dc.date.accessioned2017-01-30T12:05:13Z
dc.date.available2017-01-30T12:05:13Z
dc.date.created2010-03-09T20:02:49Z
dc.date.issued2009
dc.identifier.citationXu, T. and Lu, C. and Liu, Wan-quan. 2009. The matrix form for weighted linear discriminant analysis and fractional linear discriminant analysis, in Lai, L. L. (ed), ICMLC 2009, Jul 12 2009, pp. 1621-1627.Baoding, Hebei, China: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/17966
dc.description.abstract

In this paper we will extend the recently proposed weighted linear discriminant analysis (W_LDA) and fraction-step linear discriminant analysis (F_LDA) from one dimension vector form to the case of two dimension matrix form, which are called weighted two dimensional linear discriminant analysis (W_2DLDA) and fraction-step two dimension linear discriminant analysis (F_2DLDA), respectively. The motivation of this work is based on the recent research results on two dimensional principal component analysis (2DPCA) and 2DLDA showing that the two dimensional algorithms can save computational costs significantly and thus improve the classifiers performances. First, we derived these numerical algorithms in matrix form and then we implement these two new algorithms on ORL and YALE face databases. The experimentation results show that W_2DLDA produces the best performance among F_2DLDA, F_LDA and W_LDA.

dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5212309
dc.subjecttwo dimensional linear discriminant analysis
dc.subjectface recognition
dc.subjectweighted linear discriminant analysis
dc.subjectfraction-step linear discriminant analysis
dc.subjectlinear discriminant analysis
dc.titleThe matrix form for weighted linear discriminant analysis and fractional linear discriminant analysis
dc.typeConference Paper
dcterms.source.startPage1621
dcterms.source.endPage1627
dcterms.source.titleProceedings of the 8th International conference on Machine learning and Cybernetics
dcterms.source.seriesProceedings of the 8th International conference on Machine learning and Cybernetics
dcterms.source.isbn9781424437030
dcterms.source.conferenceICMLC 2009
dcterms.source.conference-start-dateJul 12 2009
dcterms.source.conferencelocationBaoding, Hebei, China
dcterms.source.placeUSA
curtin.note

Copyright © 2009 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

curtin.accessStatusOpen access
curtin.facultySchool of Science and Computing
curtin.facultyDepartment of Computing
curtin.facultyFaculty of Science and Engineering


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