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arXiv · 1303.0341

Matrix Completion via Max-Norm Constrained Optimization

T. Tony Cai·Wen-Xin Zhou
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Abstract

Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting. However, the uniform sampling model is unrealistic for a range of applications and the standard trace-norm relaxation can behave very poorly when the underlying sampling scheme is non-uniform. In this paper we propose and analyze a max-norm constrained empirical risk minimization method for noisy matrix completion under a general sampling model. The optimal rate of convergence is established under the Frobenius norm loss in the context of approximately low-rank matrix reconstruction. It is shown that the max-norm constrained method is minimax rate-optimal and yields a unified and robust approximate recovery guarantee, with respect to the sampling distributions. The computational effectiveness of this method is also discussed, based on first-order algorithms for solving convex optimizations involving max-norm regularization.

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BibTeXRIS

T. Tony Cai, Wen-Xin Zhou. 2013-03-02. Matrix Completion via Max-Norm Constrained Optimization. https://doi.org/10.1214/16-ejs1147

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