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Indexed by:期刊论文

Date of Publication:2013-12-10

Journal:Journal of Information and Computational Science

Included Journals:EI、Scopus

Volume:10

Issue:18

Page Number:5859-5866

ISSN No.:15487741

Abstract:Subspace learning has been widely applied to face recognition, data clustering and pattern analysis. It is particularly important to supervised learning methods. To deal with the problem of lacking local features in many supervised dimensionality reduction methods, we propose a new supervised dimensionality reduction method called Local Topological Linear Discriminant Analysis (LTLDA) We apply the local topological structure of within-class to the original LDA method. The experimental results show that our method is more efficient to LDA and Maximum Margin Criterion. ? 2013 Binary Information Press.