Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization

  title={Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization},
  author={Edward F. Gonzalez and Yin Zhang},
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 [7]. 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. 
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