An alternating projected gradient algorithm for nonnegative matrix factorization

被引:15
|
作者
Lin, Lu [1 ]
Liu, Zhong-Yun [2 ]
机构
[1] Xiamen Univ, Sch Math Sci, Xiamen 361005, Peoples R China
[2] Changsha Univ Sci & Technol, Sch Math, Changsha 410076, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonnegative matrix factorization; Projected gradient algorithm; Multiplicative updating method; Low-rank decomposition; HERMITIAN SPLITTING METHODS; DEFINITE LINEAR-SYSTEMS;
D O I
10.1016/j.amc.2011.04.070
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Due to the extensive applications of nonnegative matrix factorizations (NMFs) of nonnegative matrices, such as in image processing, text mining, spectral data analysis, speech processing, etc., algorithms for NMF have been studied for years. In this paper, we propose a new algorithm for NMF, which is based on an alternating projected gradient (APG) approach. In particular, no zero entries appear in denominators in our algorithm which implies no breakdown occurs, and even if some zero entries appear in numerators new updates can always be improved in our algorithm. It is shown that the effect of our algorithm is better than that of Lee and Seung's algorithm when we do numerical experiments on two known facial databases and one iris database. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:9997 / 10002
页数:6
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