Fusing Eigenvalues
Shahab Basiri, Esa Ollila, Gordana Draskovic, Frédéric Pascal
Abstract
In this paper, we propose a new regularized (penalized) covariance matrix estimator which encourages grouping of the eigenvalues by penalizing large differences (gaps) between successive eigenvalues. This is referred to as fusing eigenvalues (eFusion). The proposed penalty function utilizes Tukey's biweight function that is widely used in robust statistics. The main advantage of the proposed method is that it has very small bias for sufficiently large values of penalty parameter. Hence, the method provides accurate grouping of eigenvalues. Such benefits of the proposed method are illustrated with a numerical example, where the method is shown to perform favorably compared to a state-of-art method.
BibTeX
@inproceedings{icassp2019_fusingeigenvalue,
title = {Fusing Eigenvalues},
author = {Shahab Basiri and Esa Ollila and Gordana Draskovic and Frédéric Pascal},
booktitle = {ICASSP 2019},
year = {2019}
}