I uncorrelated locality preserving projections analysis based on maximum margin criterion

被引：0
作者：
机构：
[1] College of Mathematics and Statistics, Chongqing University
来源：
Tang, P.-F. (tpf@cqu.edu.cn) | 1600年 / Science Press, 18,Shuangqing Street,Haidian, Beijing, 100085, China卷 / 39期
关键词：
Face recognition; Feature extraction; Locality preserving projections (LPP); Maximum margin criterion (MMC); Uncorrelated discriminant analysis;
D O I：
10.3724/SP.J.1004.2013.01575
中图分类号：
学科分类号：
摘要：
Locality preserving projections (LPP) algorithm can only preserve nearest local quantity, so within-class and between-class local scatter matrices are introduced for characterizing the manifold structure better, and a method called uncorrelated locality preserving projection analysis based on effective and stable maximum margin criterion (MMC) is proposed. When maximizing the trace difference of scatter matrix, weight the within-class and between-class local scatter matrix through a regularized parameter so as to find the better classification subspace and avoid small sample problem. More importantly, the discriminant feature set based on the MMC is generally statistical correlated, which makes the feature information be redundant, so an uncorrelated constraint was added in the paper and the uncorrelated discriminant feature set is extracted by the derived formulas, which are more favorable for the correct recognition. Ultimately experiments on Yale, PIE face database and MNIST handwritten digit database show that the method in this paper is effective and stable and has a higher correct recognition rate compared with the LPP, LDA (Linear discriminant analysis) and LPMIP (Locality-preserved maximum information projection). © 2013 Acta Automatica sinica. All rights reserved.
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页码：1575 / 1580
页数：5
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