Low-Rank Factorization for Rank Minimization with Nonconvex Regularizers

13 Jun 2020April SaganJohn E. Mitchell

Rank minimization is of interest in machine learning applications such as recommender systems and robust principal component analysis. Minimizing the convex relaxation to the rank minimization problem, the nuclear norm, is an effective technique to solve the problem with strong performance guarantees... (read more)

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