Low-rank matrix completion with noisy observations: A quantitative comparison

Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh

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

Abstract

We consider a problem of significant practical importance, namely, the reconstruction of a low-rank data matrix from a small subset of its entries. This problem appears in many areas such as collaborative filtering, computer vision and wireless sensor networks. In this paper, we focus on the matrix completion problem in the case when the observed samples are corrupted by noise. We compare the performance of three state-of-the-art matrix completion algorithms (OptSpace, ADMiRA and FPCA) on a single simulation platform and present numerical results. We show that in practice these efficient algorithms can be used to reconstruct real data matrices, as well as randomly generated matrices, accurately.

Original languageEnglish (US)
Title of host publication2009 47th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2009
Pages1216-1222
Number of pages7
DOIs
StatePublished - 2009
Event2009 47th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2009 - Monticello, IL, United States
Duration: Sep 30 2009Oct 2 2009

Publication series

Name2009 47th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2009

Other

Other2009 47th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2009
CountryUnited States
CityMonticello, IL
Period9/30/0910/2/09

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering
  • Communication

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