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论文类型：期刊论文

发表时间：2013-12-10

发表刊物：Journal of Information and Computational Science

收录刊物：EI、Scopus

卷号：10

期号：18

页面范围：5859-5866

ISSN号：15487741

摘要：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.