Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization
Gonzalez, Edward F.
Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and Seung. In this paper we present a variation of one of the Lee-Seung algorithms with a notably improved performance. We also show that algorithms of this type do not necessarily converge to local minima.
Citable link to this pagehttps://hdl.handle.net/1911/102034
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- CAAM Technical Reports