A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators
Q. Hoarau, Arnaud Breloy, Guillaume Ginolhac, Abdourrahmane M. Atto, Jean-Marie Nicolas
Abstract
Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered application. Thus, finding a generic rule that is optimal for every criterion and/or data configurations is not straightforward. This issue is addressed in this paper for undersampled configurations (number of samples lower than the dimension of the data). The paper proposes a new regularisation parameter selection based on a subspace reduction approach. The performance of this method is investigated in terms of estimation accuracy and for adaptive detection purposes, both on simulation and real data.
BibTeX
@inproceedings{icassp2017_asubspaceapproac,
title = {A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators},
author = {Q. Hoarau and Arnaud Breloy and Guillaume Ginolhac and Abdourrahmane M. Atto and Jean-Marie Nicolas},
booktitle = {ICASSP 2017},
year = {2017}
}