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Riemannian Perspective on Matrix Factorization
Kwangjun Ahn, Felipe Suarez · 2021-02-02 · via stat.ML updates on arXiv.org

We study the non-convex matrix factorization approach to matrix completion via Riemannian geometry. Based on an optimization formulation over a Grassmannian manifold, we characterize the landscape based on the notion of principal angles between subspaces. For the fully observed case, our results show that there is a region in which the cost is geodesically convex, and outside of which all critical points are strictly saddle. We empirically study the partially observed case based on our findings.

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