Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point

ICASSP 2019
Pages: 2907 - 2911
Published: May 12, 2019
Abstract
In this paper, we use the perturbed gradient based alternating minimization for solving a class of low-rank matrix factorization problems. Alternating minimization is a simple but popular approach which has been applied to problems in optimization, machine learning, data mining, and signal processing, etc. By leveraging the block structure of the problem, the algorithm updates two blocks of variables in an alternating manner. For the nonconvex...
Paper Details
Title
Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point
Published Date
May 12, 2019
Journal
Pages
2907 - 2911
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