Joint inverse covariances estimation with mutual linear structure

Ilya Soloveychik; Ami Wiesel

doi:10.1109/EUSIPCO.2015.7362685

Joint inverse covariances estimation with mutual linear structure

Ilya Soloveychik, Ami Wiesel

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We consider the problem of joint estimation of structured inverse covariance matrices. We assume the structure is unknown and perform the estimation using groups of measurements coming from populations with different covariances. Given that the inverse covariances 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 estimation of the inverse covariances. We propose a novel optimization algorithm discovering and exploring the underlying structure and provide its efficient implementation. Numerical simulations are presented to illustrate the performance benefits of the proposed algorithm.

Original language	English
Title of host publication	2015 23rd European Signal Processing Conference, EUSIPCO 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1756-1760
Number of pages	5
ISBN (Electronic)	9780992862633
DOIs	https://doi.org/10.1109/EUSIPCO.2015.7362685
State	Published - 22 Dec 2015
Event	23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France Duration: 31 Aug 2015 → 4 Sep 2015

Publication series

Name	2015 23rd European Signal Processing Conference, EUSIPCO 2015

Conference

Conference	23rd European Signal Processing Conference, EUSIPCO 2015
Country/Territory	France
City	Nice
Period	31/08/15 → 4/09/15

Bibliographical note

Publisher Copyright:
© 2015 EURASIP.

Keywords

Structured inverse covariance estimation
graphical models
joint inverse covariance estimation

Access to Document

10.1109/EUSIPCO.2015.7362685

Cite this

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abstract = "We consider the problem of joint estimation of structured inverse covariance matrices. We assume the structure is unknown and perform the estimation using groups of measurements coming from populations with different covariances. Given that the inverse covariances 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 estimation of the inverse covariances. We propose a novel optimization algorithm discovering and exploring the underlying structure and provide its efficient implementation. Numerical simulations are presented to illustrate the performance benefits of the proposed algorithm.",

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Soloveychik, I & Wiesel, A 2015, Joint inverse covariances estimation with mutual linear structure. in 2015 23rd European Signal Processing Conference, EUSIPCO 2015., 7362685, 2015 23rd European Signal Processing Conference, EUSIPCO 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1756-1760, 23rd European Signal Processing Conference, EUSIPCO 2015, Nice, France, 31/08/15. https://doi.org/10.1109/EUSIPCO.2015.7362685

Joint inverse covariances estimation with mutual linear structure. / Soloveychik, Ilya; Wiesel, Ami.
2015 23rd European Signal Processing Conference, EUSIPCO 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1756-1760 7362685 (2015 23rd European Signal Processing Conference, EUSIPCO 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Joint inverse covariances estimation with mutual linear structure

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this