UM
Maximum Correntropy Criterion for Convex and Semi-Nonnegative Matrix Factorization
Qin, Anyong; Shang, Zhaowei; Tian, Jinyu; Li, Ailin; Wang, Yulong; Tang, Yuan Yan; IEEE
2017
Conference Name2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Pages1856-1861
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractMatrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not depend on the assumption of Gaussianity, which the mean square error (MSE) heavily depends on. In this paper, we propose two novel algorithms, called Maximum Correntropy Criterion based Convex and Semi-Nonnegative Matrix Factorization (MCC-ConvexNMF, MCCSemiNMF). Compared with the mean square error based convex and semi-nonnegative matrix factorization, the proposed methods can extract more information from the data and produce more accurate solutions. Experimental results on both synthetic dataset and the popular face database illustrate the effectiveness of our methods.
KeywordMaximum correntropy criterion Nonnegative matrix factorization clustering
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000427598701152
The Source to ArticleWOS
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Qin, Anyong,Shang, Zhaowei,Tian, Jinyu,et al. Maximum Correntropy Criterion for Convex and Semi-Nonnegative Matrix Factorization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:1856-1861.
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