A Riemannian gossip approach to decentralized matrix completion
  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. 
Bamdev Mishra, Hiroyuki Kasai, and Atul Saroop
cs.NA cs.LG math.OC