Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses

Abstract

We analyze matrix-sensing landscapes far from the ground truth, where spurious solutions can obstruct local optimization. Under suitable conditions, distant critical points are strict saddles rather than local minima, and higher-order losses amplify the negative curvature around them. This provides a theoretical alternative to increasing model complexity through overparametrization.