Decentralized low-rank matrix completion

Qing Ling, Yangyang Xu, Wotao Yin, Zaiwen Wen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

Abstract

This paper introduces algorithms for the decentralized low-rank matrix completion problem. Assume a low-rank matrix W = [W 1,W 2, ...,W L]. In a network, each agent ℓ observes some entries of W . In order to recover the unobserved entries of W via decentralized computation, we factorize the unknown matrix W as the product of a public matrix X, common to all agents, and a private matrix Y = [Y 1,Y 2, ...,Y L], where Y is held by agent ℓ. Each agent ℓ alternatively updates Y and its local estimate of X while communicating with its neighbors toward a consensus on the estimate. Once this consensus is (nearly) reached throughout the network, each agent ℓ recovers W = XY , and thus W is recovered. The communication cost is scalable to the number of agents, and W and Y are kept private to agent ℓ to a certain extent. The algorithm is accelerated by extrapolation and compares favorably to the centralized code in terms of recovery quality and robustness to rank over-estimate.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2925-2928
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • decentralized algorithm
  • low-rank matrix completion
  • matrix factorization
  • privacy protection

Fingerprint

Dive into the research topics of 'Decentralized low-rank matrix completion'. Together they form a unique fingerprint.

Cite this