Nonnegative Matrix Factorization via Newton Iteration for Shared-memory Systems∗
نویسنده
چکیده
Nonnegative Matrix Factorization (NMF) can be used to approximate a large nonnegative matrix as a product of two smaller nonnegative matrices. This paper shows in detail how an NMF algorithm based on Newton iteration can be derived utilizing the general Karush-KuhnTucker (KKT) conditions for first-order optimality. This algorithm is suited for parallel execution on shared-memory systems. It was implemented and tested, delivering satisfactory speedup results.
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