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Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons
2008
2008 Eighth IEEE International Conference on Data Mining
Nonnegative Matrix Factorization (NMF) is a dimension reduction method that has been widely used for various tasks including text mining, pattern analysis, clustering, and cancer class discovery. The mathematical formulation for NMF appears as a non-convex optimization problem, and various types of algorithms have been devised to solve the problem. The alternating nonnegative least squares (ANLS) framework is a block coordinate descent approach for solving NMF, which was recently shown to be
doi:10.1109/icdm.2008.149
dblp:conf/icdm/KimP08
fatcat:ubq4sb3o7fa55lgpiqxpvixtuu