Indexed on: 01 Sep '20Published on: 31 Aug '20Published in: arXiv - Statistics - Machine Learning
Existing results for low-rank matrix recovery largely focus on quadratic loss, which enjoys favorable properties such as restricted strong convexity/smoothness (RSC/RSM) and well conditioning over all low rank matrices. However, many interesting problems involve non-quadratic loss do not satisfy such properties; examples including one-bit matrix sensing, one-bit matrix completion, and rank aggregation. For these problems, standard nonconvex approaches such as projected gradient with rank constraint alone (a.k.a. iterative hard thresholding) and Burer-Monteiro approach may perform badly in practice and have no satisfactory theory in guaranteeing global and efficient convergence. In this paper, we show that the critical component in low-rank recovery with non-quadratic loss is a regularity projection oracle, which restricts iterates to low-rank matrix within an appropriate bounded set, over which the loss function is well behaved and satisfies a set of relaxed RSC/RSM conditions. Accordingly, we analyze an (averaged) projected gradient method equipped with such an oracle, and prove that it converges globally and linearly. Our results apply to a wide range of non-quadratic problems including rank aggregation, one bit matrix sensing/completion, and more broadly generalized linear models with rank constraint.