An Alternating Minimization Algorithm for Non-negative Matrix Approximation
نویسنده
چکیده
Matrix approximation problems with non-negativity constraints arise during the analysis of high-dimensional non-negative data. Applications include dimensionality reduction, feature extraction and statistical factor analysis. This paper discusses a general algorithm for solving these problems with respect to a large class of error measures. The primary goal of this article is to develop a rigorous convergence theory for the algorithm. It also touches on some experimental results of S. Sra [1] which indicate the superiority of this method to some previously proposed techniques. Date: 8 May 2003. J. Tropp is with The University of Texas, Austin, TX 78712 USA, [email protected]. 1 NON-NEGATIVE MATRIX APPROXIMATION 2
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تاریخ انتشار 2003