Abstract
l 1 -norm penalty and noise-free approach are considered in this paper to contribute to a maximum correntropy criterion (MCC) based algorithm. The introduced $l$ 1 -norm constrained noise-free MCC (L 1 -NFMCC) algorithm inherits the good behavior of MCC in non-Gaussian environments. The cost function of the L 1 -NFMCC algorithm is created by introducing $l$ 1 -norm penalty into the traditional cost function of MCC. In this regard, the L 1 -NFMCC algorithm can fully use the sparse characteristics which exist in many real systems. In addition, the noise-free method is used in the L 1 -NFMCC algorithm to provide a variable convergence step (VCS). The VCS is obtained by minimizing the noise-free (NF) a posteriori error signal with respect to the convergence step. As a consequence, the proposed L 1 -NFMCC algorithm holds an excellent mean square deviation (MSD) behavior. Meanwhile, it shows particularly good property in sparse system. Numerical simulations are utilized to investigate the superiority of the L 1 -NFMCC algorithm in non-Gaussian noises.
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