Computer Science ›› 2015, Vol. 42 ›› Issue (7): 280-284, 304.doi: 10.11896/j.issn.1002-137X.2015.07.060

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Constrained Nonnegative Matrix Factorization with Sparseness for Image Representation

HU Xue-kao, SUN Fu-ming and LI Hao-jie   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Matrix decomposition is widely applied in many domains since it is exploited to process the large-scale data.To the best of our knowledge,nonnegative matrix factorization (NMF) is a non-negative decomposition method under the condition that constraint matrix elements are non-negative.By using the informati on provided by a few known labeled examples and large number of unlabeled examples as well as imposing a certain sparseness constraint on NMF, this paper put forward a method called constraint nonnegative matrix factorization with sparseness (CNMFS).In the algorithm,an effective update approach is constructed,whose convergence is proved subsequently.Extensive experiments were conducted on common face databases,and the comparisons with two state-of-the-art algorithms including CNMF and NMF demonstrate that CNMFS has superiority in both sparseness and clustering.

Key words: Nonnegative matrix factorization,Semi-supervised,Sparseness constraints

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