Error bounds for maximum likelihood matrix completion under sparse factor models

Akshay Soni, Swayambhoo Jain, Jarvis D Haupt, Stefano Gonella

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

5 Scopus citations

Abstract

This paper examines a general class of matrix completion tasks where entry wise observations of the matrix are subject to random noise or corruption. Our particular focus here is on settings where the matrix to be estimated follows a sparse factor model, in the sense that it may be expressed as the product of two matrices, one of which is sparse. We analyze the performance of a sparsity-penalized maximum likelihood approach to such problems to provide a general-purpose estimation result applicable to any of a number of noise/corruption models, and describe its implications in two stylized scenarios - one characterized by additive Gaussian noise, and the other by highly-quantized one-bit observations. We also provide some supporting empirical evidence to validate our theoretical claims in the Gaussian setting.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages399-403
Number of pages5
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

Keywords

  • Complexity regularization
  • Matrix completion
  • Maximum likelihood
  • Sparse estimation

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