Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach


Dohyung Park, Anastasios Kyrillidis, Constantine Carmanis, Sujay Sanghavi ;
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:65-74, 2017.


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 $X ∈R^m x n$ is represented as $UV^T$, where $U ∈R^m x r$ and $V ∈R^n x r$. 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.

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