Separable two-dimensional linear discriminant analysis

Jianhua ZHAO, Leung Ho Philip YU, Shulan LI

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

Several two-dimensional linear discriminant analysis LDA (2DLDA) methods have received much attention in recent years. Among them, the 2DLDA, introduced by Ye, Janardan and Li (2005), is an important development. However, it is found that their proposed iterative algorithm does not guarantee convergence. In this paper, we assume a separable covariance matrix of 2D data and propose separable 2DLDA which can provide a neatly analytical solution similar to that for classical LDA. Empirical results on face recognition demonstrate the superiority of our proposed separable 2DLDA over 2DLDA in terms of classification accuracy and computational efficiency. Copyright © 2010 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationProceedings of COMPSTAT'2010: 19th International Conference on Computational Statistics Paris France, August 22-27, 2010 keynote, invited and contributed papers
EditorsYves LECHEVALLIER, Gilbert SAPORTA
Place of PublicationHeidelberg
PublisherSpringer-Verlag Berlin Heidelberg
Pages597-604
ISBN (Electronic)9783790826043
ISBN (Print)9783790826036
DOIs
Publication statusPublished - 2010

Citation

Zhao, J., Yu, P. L. H., & Li, S. (2010). Separable two-dimensional linear discriminant analysis. In Y. Lechevallier & G. Saporta (Eds.), Proceedings of COMPSTAT'2010: 19th International Conference on Computational Statistics Paris France, August 22-27, 2010 keynote, invited and contributed papers (pp. 597-604). Heidelberg: Springer-Verlag Berlin Heidelberg.

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