Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case

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Title Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case
Author Paliwal, Kuldip Kumar; Sharma, Alok
Journal Name Journal of Pattern Recognition Research
Year Published 2011
Place of publication United States
Publisher JPRR
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.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.jprr.org/index.php/jprr/article/view/370
Volume X
Page from 298
Page to 306
ISSN 1558-884X
Date Accessioned 2012-05-30; 2012-06-26T00:52:39Z
Date Available 2012-06-26T00:52:39Z
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Artificial Intelligence and Image Processing
URI http://hdl.handle.net/10072/45639
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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