Enhanced lasso recovery on graph

By Xavier Bresson, Thomas Laurent, James von Brecht. 2015

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.

Subfields: Optimization & Training

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In Optimization & Training

1. Michael M. Bronstein
2. Emanuele Rodolà
3. Davide Boscaini
4. Jonathan Masci
5. Federico Monti

Citations in the corpus

• Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “orthogonal eigenfunctions of the Laplacian operator as a generalization of the Fourier basis. Constructions such as wavelets [14, 18, 21, 39, 45] or algorithms such as dictionary learning [62], Lasso [9], PCA [43, 44], or matrix completion [23] originally developed for the Euclidean domain, were als”