Fast Low-Rank Matrix Learning with Nonconvex Regularization

3 Dec 2015Quanming YaoJames T. KwokWenliang Zhong

Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance... (read more)

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