| We establish theoretical recovery guarantees of a family of Riemannian optimization algorithms for low rank matrix recovery, which is about recovering an m×n rank r matrix from p<mn number of linear measurements. The algorithms are first interpreted as the iterative hard thresholding algorithms with subspace projections.... |
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