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We consider one bit matrix completion under rank constraint. We present an estimator based on rank constrained maximum likelihood estimation, and an ecient greedy algorithm to solve it approximately based on an extension of conditional gradient descent. The output of the proposed algorithm converges at a linear rate to the underlying true low-rank matrix up(More)
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