Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?

Abstract

We explore the local landscape of low-rank matrix recovery, focusing on reconstructing a $d_1\times d_2$ matrix $X^{\star }$ with rank $r$ from $m$ linear measurements, some potentially noisy. When the noise is distributed according to an outlier model, minimizing a nonsmooth ${\ell }_1$-loss with a simple sub-gradient method can often perfectly recover the ground truth matrix $X^{\star }$. Given this, a natural question is what optimization property (if any) enables such learning behavior. The most plausible answer is that the ground truth $X^{\star }$ manifests as a local optimum of the loss function. In this paper, we provide a strong negative answer to this question, showing that, under moderate assumptions, the true solutions corresponding to $X^{\star }$ do not emerge as local optima, but rather as strict saddle points -- critical points with strictly negative curvature in at least one direction. Our findings challenge the conventional belief that all strict saddle points are undesirable and should be avoided.