Enhanced Lasso Recovery on Graph
Machine Learning
2015-06-22 v1 Machine Learning
Abstract
This work aims at recovering signals that are sparse on graphs. Compressed sensing offers techniques for signal recovery from a few linear measurements and graph Fourier analysis provides a signal representation on graph. In this paper, we leverage these two frameworks to introduce a new Lasso recovery algorithm on graphs. More precisely, we present a non-convex, non-smooth algorithm that outperforms the standard convex Lasso technique. We carry out numerical experiments on three benchmark graph datasets.
Cite
@article{arxiv.1506.05985,
title = {Enhanced Lasso Recovery on Graph},
author = {Xavier Bresson and Thomas Laurent and James von Brecht},
journal= {arXiv preprint arXiv:1506.05985},
year = {2015}
}