稀疏性正则化非负矩阵分解的在线学习方法

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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.

Key words： Sparse Regularization Non-Negative Matrix Factorization Block-Coordinate Descent Online Learning

Received: 14 November 2011

ZTFLH:	TP391
	TN911.73

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	XUE Mo-Gen
	XU Guo-Ming
	WANG Feng

Cite this article:

XUE Mo-Gen,XU Guo-Ming,WANG Feng. Sparse Regularized Non-Negative Matrix Factorization through Online Learning[J]. , 2013, 26(3): 242-246.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2013/V26/I3/242

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