Affiliation:
1. Department of Mathematics and Operational Research, Faculté Polytechnique, Université de Mons, 7000 Mons, Belgium
Abstract
We propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapolation in the context of the exact coordinate descent algorithms tackling the nonconvex NMF problems is novel. We illustrate the performance of this approach on two state-of-the-art NMF algorithms: accelerated hierarchical alternating least squares and alternating nonnegative least squares, using synthetic, image, and document data sets.
Subject
Cognitive Neuroscience,Arts and Humanities (miscellaneous)
Cited by
29 articles.
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