Joint estimation of inverse covariance matrices lying in an unknown subspace

Ilya Soloveychik, Ami Wiesel

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of joint estimation of inverse covariance matrices lying in an unknown subspace of the linear space of symmetric matrices. We perform the estimation using groups of measurements with different covariances. Assuming the inverse covariances span a low-dimensional subspace, our aim is to determine this subspace and to exploit this knowledge in order to improve the estimation. We develop a novel optimization algorithm discovering and exploiting the underlying low-dimensional subspace. We provide a computationally efficient algorithm and derive a tight upper performance bound. Numerical simulations on synthetic and real world data are presented to illustrate the performance benefits of the algorithm.

Original languageAmerican English
Article number7815401
Pages (from-to)2379-2388
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume65
Issue number9
DOIs
StatePublished - 1 May 2017

Keywords

  • Inverse covariance estimation
  • joint inverse covariance estimation

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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