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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.


First published in the Proceedings of the 23rd European Signal Processing Conference (EUSIPCO-2015) in 2015, published by EURASIP.

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This article is provided for educational and research purposes. More info can be found about the Signal Processing Conference (EUSIPCO), 2015 23rd European at

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X. Bresson, T. Laurent and J. von Brecht. "Enhanced Lasso Recovery." Graph European Signal Processing Conference (EUSIPCO), 2015.

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