Abstract
Focal Underdetermined System Solver (FOCUSS) is a powerful tool for sparse representation and underdetermined inverse problems. In this correspondence, we strengthen the FOCUSS method with the following main contributions: 1) we give a more rigorous derivation of the FOCUSS for the sparsity parameter 0 < p < 1 by a nonlinear transform and 2) we develop the CG-FOCUSS by incorporating the conjugate gradient (CG) method to the FOCUSS, which significantly reduces a computational cost with respect to the standard FOCUSS and extends its availability for large scale problems. We justify the CG-FOCUSS based on a probability theory. Furthermore, the high performance of the CG-FOCUSS is demonstrated with experiments.
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