@inproceedings{57dad5685732402fab79accc3ef68f76,
title = "Joint inverse covariances estimation with mutual linear structure",
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.",
keywords = "Structured inverse covariance estimation, graphical models, joint inverse covariance estimation",
author = "Ilya Soloveychik and Ami Wiesel",
note = "Publisher Copyright: {\textcopyright} 2015 EURASIP.; 23rd European Signal Processing Conference, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
year = "2015",
month = dec,
day = "22",
doi = "10.1109/EUSIPCO.2015.7362685",
language = "الإنجليزيّة",
series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1756--1760",
booktitle = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
address = "الولايات المتّحدة",
}