Weighted SPICE: A Unifying Approach for Hyperparameter-Free Sparse Estimation
Statistics Theory
2015-05-12 v1 Statistics Theory
Abstract
In this paper we present the SPICE approach for sparse parameter estimation in a framework that unifies it with other hyperparameter-free methods, namely LIKES, SLIM and IAA. Specifically, we show how the latter methods can be interpreted as variants of an adaptively reweighted SPICE method. Furthermore, we establish a connection between SPICE and the l1-penalized LAD estimator as well as the square-root LASSO method. We evaluate the four methods mentioned above in a generic sparse regression problem and in an array processing application.
Cite
@article{arxiv.1406.7698,
title = {Weighted SPICE: A Unifying Approach for Hyperparameter-Free Sparse Estimation},
author = {Petre Stoica and Dave Zachariah and Jian Li},
journal= {arXiv preprint arXiv:1406.7698},
year = {2015}
}