We show that the Riemannian gradient descent algorithm on the low-rank matrix manifold almost surely escapes some spurious critical points on the boundary of the manifold. Given that the low-rank matrix manifold is an incomplete set, this result is the first to overcome this difficulty and partially justify the global use of the Riemannian gradient descent on the manifold. The spurious critical points are some rank-deficient matrices that capture only part of the SVD components of the ground truth. They exhibit very singular behavior and evade the classical analysis of strict saddle points. We show that using the dynamical low-rank approximation and a rescaled gradient flow, some of the spurious critical points can be converted to classical strict saddle points, which leads to the desired result. Numerical experiments are provided to support our theoretical findings.
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