Computer Science ›› 2017, Vol. 44 ›› Issue (6): 298-305.doi: 10.11896/j.issn.1002-137X.2017.06.053

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Graph Regularized and Incremental Nonnegative Matrix Factorization with Sparseness Constraints

SUN Jing, CAI Xi-biao, JIANG Xiao-yan and SUN Fu-ming   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Nonnegative matrix factorization (NMF) not only is a description of the data,but also has intuitive physical meaning after the decomposition of the matrix.With the aim to enhance the validity and classification accuracy,a more reasonable algorithm was proposed,which is graph regularized and incremental nonnegative matrix factorization with sparseness constraints (GINMFSC).It not only preserves the intrinsic geometry of data,but also makes full use of the last step decomposition results as incremental learning,and introduces sparseness constraint to coefficient matrix.Finally,they are integrated into one single objective function and an efficient updating approach is produced.Compared with NMF,GNMF,INMF and IGNMF,experiments on several databases have shown that the proposed method achieves better clustering accuracy and sparsity while reducing the computation time.

Key words: Nonnegative matrix factorization,Graph regularized,Sparseness constraints,Incremental learning

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