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Learning the Informative Components in Nonnegative Matrix Factorization
Miao Cheng; Bin Fang; Jing Chen; Weibin Yang; Yuan Yan Tang
2010-12-01
Conference Name2010 Chinese Conference on Pattern Recognition (CCPR)
Source Publication2010 Chinese Conference on Pattern Recognition, CCPR 2010 - Proceedings
Pages1052-1056
Conference Date21-23 Oct. 2010
Conference PlaceChongqing
CountryChina
PublisherIEEE
Abstract

In order to exploit the informative components hidden in nonnegative matrix factorization, an information theoretic learning method, termed ITNMF, is presented. Different from the existing NMF methods, the proposed method is able to handle the general objective optimization, and takes the conjugate gradient technique to enhance the iterative optimization. To tackle the null matrix factorization problem, the line search approach adopts the insured conditions while keeping the feasible step descendent. In addition, the function value based stopping rule is employed to achieve optimized efficiency. Experiments of pattern classification on the data sets under variant pose and illumination conditions reveal that the proposed method can outperform the existing methods. 

KeywordGradient Method Information Theoretic Learning Line Search Approach Nonnegative Matrix Factorization (Nmf) Stopping Rule
DOIhttps://doi.org/10.1109/CCPR.2010.5659274
URLView the original
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer Science, Chongqing University, Chongqing 400030, China
Recommended Citation
GB/T 7714
Miao Cheng,Bin Fang,Jing Chen,et al. Learning the Informative Components in Nonnegative Matrix Factorization[C]:IEEE,2010:1052-1056.
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