Approximate LDA technique for dimensionality reduction in the small sample size case

Paliwal, K.K. and Sharma, Alokanand (2011) Approximate LDA technique for dimensionality reduction in the small sample size case. Journal of Pattern Recognition Research, 6 (2). pp. 298-306. ISSN 1558-884X

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Abstract

The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Ms Shalni Sanjana
Date Deposited:	21 Jun 2011 09:01
Last Modified:	21 Jun 2012 09:01
URI:	https://repository.usp.ac.fj/id/eprint/4802

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