Enhanced Lasso Recovery on Graph
This work addresses signal recovery for applications involving graph-structured data, representing an incremental improvement over existing methods.
The paper tackles the problem of recovering sparse signals on graphs by introducing a new non-convex, non-smooth Lasso algorithm that outperforms the standard convex Lasso technique, as demonstrated through numerical experiments on three benchmark graph datasets.
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.