SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm
Information Theory
2009-01-08 v1 math.IT
Abstract
We develop a Recursive -Regularized Least Squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an Expectation-Maximization type algorithm. Simulation studies in the context of channel estimation, employing multi-path wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.
Cite
@article{arxiv.0901.0734,
title = {SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm},
author = {Behtash Babadi and Nicholas Kalouptsidis and Vahid Tarokh},
journal= {arXiv preprint arXiv:0901.0734},
year = {2009}
}