Joint covariance estimation with mutual linear structure

Ilya Soloveychik, Ami Wiesel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We consider the joint estimation of structured covariance matrices. We assume the structure is unknown and perform the estimation using heterogeneous training sets. More precisely, we are given groups of measurements coming from centered normal populations with different covariance matrices. Assuming that all these covariance matrices span a low dimensional affine subspace in the space of symmetric matrices, our aim is to determine this structure. It is then utilized to improve the covariance estimation. We provide an algorithm discovering and exploring the underlying covariance structure and analyze its error bounds. Numerical simulations are presented to illustrate the performance benefits of the proposed algorithm.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3437-3441
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - 4 Aug 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 19 Apr 201424 Apr 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period19/04/1424/04/14

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Structured covariance estimation
  • joint covariance estimation

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