MIST: L0 sparse linear regression with momentum

Goran Marjanovic; Magnus O. Ulfarsson; Alfred O. Hero III

ICASSP 2015accepted0 citations

MIST: L0 sparse linear regression with momentum

Goran Marjanovic, Magnus O. Ulfarsson, Alfred O. Hero III

Abstract

Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty induces sparsity and the natural choice is the so-called l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> norm. In this paper we develop a Momentumized Iterative Shrinkage Thresholding (MIST) algorithm for minimizing the resulting non-convex criterion and prove its convergence to a local minimizer. Simulations on large data sets show superior performance of the proposed method to other methods.

BibTeX

@inproceedings{icassp2015_mistl0sparseline,
  title = {MIST: L0 sparse linear regression with momentum},
  author = {Goran Marjanovic and Magnus O. Ulfarsson and Alfred O. Hero III},
  booktitle = {ICASSP 2015},
  year = {2015}
}