@inproceedings{426105d8ad8044b08ba00e5293beb057,
title = "Joint covariance estimation with mutual linear structure",
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.",
keywords = "Structured covariance estimation, joint covariance estimation",
author = "Ilya Soloveychik and Ami Wiesel",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 ; Conference date: 19-04-2014 Through 24-04-2014",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICASSP.2015.7178609",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3437--3441",
booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",
address = "الولايات المتّحدة",
}