A Riemannian gossip approach to decentralized matrix completion

Abstract

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem. The proposed approach is on the Riemannian Grassmann manifold that allows local matrix completion by different agents while achieving asymptotic consensus on the global low-rank factors. The resulting approach is scalable and parallelizable. Our numerical experiments show the good performance of the proposed algorithms on various benchmarks.

Under review

References in corpus (1)

• Manopt, a Matlab toolbox for optimization on manifolds

Cited by in corpus (1)

• A Riemannian gossip approach to subspace learning on Grassmann manifold