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Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:65-74, 2017.
Abstract
We consider the non-square matrix sensing problem, under restricted isometry property (RIP) assumptions. We focus on the non-convex formulation, where any rank-r matrix X∈Rmxn is represented as UVT, where U∈Rmxr and V∈Rnxr. In this paper, we complement recent findings on the non-convex geometry of the analogous PSD setting [5], and show that matrix factorization does not introduce any spurious local minima, under RIP.