文章基本信息

标题：LDA-based Non-negative Matrix Factorization for Supervised Face Recognition
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作者：Xue, Yun ; Tong, Chong Sze ; Yuan, Jing Yun 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：5
页码：1294-1301
DOI：10.4304/jsw.9.5.1294-1301
语种：English
出版社：Academy Publisher
摘要：In PCA based face recognition, the basis imagesmay contain negative pixels and thus do not facilitatephysical interpretation. Recently, the technique of nonnegativematrix Factorization (NMF) has been applied to face recognition: the non-negativity constraint of NMF yieldsa localized parts-based representation which achieves arecognition rate that is on par with the eigenface approach. In this paper, we propose a new variation of the NMF algorithm that incorporates training information in a supervised learning setting. We integrate an additional term basedon Fisher’s Linear Discriminant Analysis into the NMF algorithm and prove that our new update rule can maintainthe non-negativity constraint under a mild condition andhence preserve the intuitive meaning for the base vectors and weight vectors while facilitating the supervised learning of within-class and between-class information. We tested our new algorithm on the well-known ORL database, CMU PIE database and FERET database, and the results from experiments are very encouraging compared with traditional techniques including the original NMF, the Eigenface method, the sequential NMF+LDA method and the Fisherface method.
关键词：nonnegative matrix factorization;principal component analysis;fisher linear discriminant analysis;eigenface;fisherface