PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Surveying and comparing simultaneous sparse approximation (or group lasso) algorithms
Alain Rakotomamonjy
Signal Processing Volume 91, pp. 1505-1526, 2011.

Abstract

In this paper, we survey and compare different algorithms that, given an overcomplete dictionary of elementary functions, solve the problem of simultaneous sparse signal approximation, with common sparsity profile induced by a lp-lq mixed-norm. Such a problem is also known in the statistical learning community as the group lasso problem. We have gathered and detailed different algorithmic results concerning these two equivalent approximation problems. We have also enriched the discussion by providing relations between several algorithms. Experimental comparisons of the detailed algorithms have also been carried out. The main lesson learned from these experiments is that depending on the performance measure, greedy approaches and iterative reweighted algorithms are the most efficient algorithms either in term of computational complexities, sparsity recovery or mean-square error.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7553
Deposited By:Alain Rakotomamonjy
Deposited On:17 March 2011