Abstract：In order to overcome the inefficiency of non-negative matrix factorization,a fast approach based on online learning for sparse regularized non-negative matrix factorization is proposed. Firstly,the objective function is defined by imposing the regularization term to control the sparsity of the coefficient matrix,and the problem is transformed into the dictionary learning problem of sparse representation. Therefore,the object function can be solved by the online dictionary learning algorithm. Then,the block-coordinate descent algorithm is used to update the matrix in every iterative process,consequently,the convergence rate is improved. The experimental results show that the proposed method effectively preserves the structure information of images and simultaneously enhances the running efficiency evidently.
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2013, Vol. 26 
