Title: Greedy methods for simultaneous sparse approximation
Abstract:This paper extends greedy methods to simultaneous sparse approximation. This problem consists in finding good esti-mation of several input signals at once, using different linear combinations of a few...This paper extends greedy methods to simultaneous sparse approximation. This problem consists in finding good esti-mation of several input signals at once, using different linear combinations of a few elementary signals, drawn from a fixed collection. The sparse algorithms for which simultaneous ver-sions are proposed are namely CoSaMP, OLS and SBR. These approaches are compared to Tropp's S-OMP algorithm using simulation signals. We show that in the case of signals ex-hibiting correlated components, the simultaneous versions of SBR and CoSaMP perform better than S-OMP and S-OLS.Read More