Abstract:We propose a new greedy sparse approximation algorithm, called SLS for Single L1 Selection, that addresses a least squares optimization problem under a cardinality constraint. The specificity and increased efficiency of SLS originate from the atom selection step, based on exploiting 1 -norm solutions. At each iteration, the regularization path of a least-squares criterion penalized by the 1 norm of the remaining variables is built. Then, the selected atom is chosen according to a scoring function defined over … Show more
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