Computer Science ›› 2015, Vol. 42 ›› Issue (7): 280-284.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

[1] Lee D D,seung H S.Learning the parts of objects by non-negative matrix factorization[J].Nature,1999,401(6755):788-791
[2] 杜世强,石玉清,王维兰,等.基于图正则化的半监督非负矩阵分解[J].计算机工程与应用,2012,8(36):194-200Du Shi-qiang,Shi Yu-qing,Wang Wei-lan,et al.Graph regulari-zed-based semi-supervised non-negative matrix factorization [J].Computer Engineering and Applications,2012,48(36):194-200
[3] Cai Deng,He Xiao-fei,Han Jia-wei,et al.Graph regularized non-negative matrix factorization for data representation[J].IEEE Trans on Pattern Anal Mach Intell,2011,33(8):1548-1560
[4] Hoyer P O.Non-negative matrix factorization with sparsenessconstrains[J].Journal of Machine Learning Research,2004,5(9):1457-1469
[5] Sun Fu-ming,Tang Jin-hui,Li Hao-jie,et al.Multi-label image categorization with sparse factor representation [J].IEEE Transaction on Image Processing,2014,23(3):1028-1037
[6] Li S Z,Hou Xin-wen,Zhang Hong-jiang,et al.Learning spatially localized,parts-based representation [C]∥Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Los Alamitos,California,USA,2001(1):207-212
[7] Liu Hai-feng,Wu Zhao-hui,Li Xue-long,et al.Constrained non-negative matrix factorization for image representation [J].IEEE Trans on Pattern Anal Mach Intell,2012,34(7):1299-1311
[8] Shahnaza F,Berrya M W,Paucab V,et al.Plemmonsb.Docu-ment clustering using nonnegative matrix factorization[J].Information Processing Management,2006,42(2):373-386
[9] Lovasz L,Plummer M.Matching Theory [M].North Holland,1986
[10] Michael W,Shakhina A,Stewart G W.Computing sparse re-duced-rank approximations to sparse matrices [J].ACM Transactions on mathematical software,2004,19(3):231-235

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