A max-norm constrained minimization approach to 1-bit matrix completion

Abstract

We consider in this paper the problem of noisy 1-bit matrix completion under a general non-uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max-norm constrained maximum likelihood estimate is introduced and studied. The rate of convergence for the estimate is obtained. Information-theoretical methods are used to… (More)

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Cite this paper

@article{Cai2013AMC, title={A max-norm constrained minimization approach to 1-bit matrix completion}, author={Tony Cai and Wen-Xin Zhou}, journal={Journal of Machine Learning Research}, year={2013}, volume={14}, pages={3619-3647} }