  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 $X \in \mathbb{R}^{m \times n}$ is represented as $UV^\top$,
where $U \in \mathbb{R}^{m \times r}$ and $V \in \mathbb{R}^{n \times r}$. 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.
