A greedy algorithm for low-rank matrix recovery incorporates subspace prior information to achieve convergence under milder rank-restricted isometry conditions than standard methods without priors.
Low-rank factorization model for matrix completion by a non-linear successive over-relaxation algorithm,
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A Greedy Algorithm for Matrix Recovery with Subspace Prior Information
A greedy algorithm for low-rank matrix recovery incorporates subspace prior information to achieve convergence under milder rank-restricted isometry conditions than standard methods without priors.