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In this paper, we propose novel gossip algorithms for the low-rankdecentralized matrix completion problem. The proposed approach is on theRiemannian Grassmann manifold that allows local matrix completion by differentagents while achieving asymptotic consensus on the global low-rank factors. Theresulting approach is scalable and parallelizable. Our numerical experimentsshow the good performance of the proposed algorithms on various benchmarks.

A Riemannian gossip approach to decentralized matrix completion