Computer Science ›› 2016, Vol. 43 ›› Issue (7): 77-82, 105.doi: 10.11896/j.issn.1002-137X.2016.07.013

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Semi-supervised Nonnegative Matrix Factorization Based on Graph Regularization and Sparseness Constraints

JIANG Xiao-yan, SUN Fu-ming and LI Hao-jie   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Nonnegative matrix factorization (NMF) is a kind of matrix factorization algorithm under non-negative constraints .With the aim to enhance the recognition rate,a method called graph regularized and constrained non-negative matrix factorization with sparseness (GCNMFS) was proposed.It not only preserves the intrinsic geometry of data,but also uses the label information for semi-supervised learning and introduces sparseness constraint into base matrix.Finally,they are integrated into a single objective function.An efficient updating approach was produced and the convergence of this algorithm was also proved.Compared with NMF,GNMF and CNMF,experiments on some face databases show that the proposed method can achieve better clustering results and sparseness.

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

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