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Convex regularized recursive maximum correntropy algorithm
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DOI：10.1016/j.sigpro.2016.05.030.png)
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Abstract
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In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced. Simulation results show that the CR-RMC can significantly outperform the original recursive maximum correntropy (RMC) algorithm especially when the underlying system is very sparse. Compared with the convex regularized recursive least squares (CR-RLS) algorithm, the new algorithm also shows strong robustness against impulsive noise. The CR-RMC also performs much better than other LMS-type sparse adaptive filtering algorithms based on MCC. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Maximum correntropy criterion (MCC)
Sparse adaptive filtering
Recursive maximum correntropy (RMC)
Convex regularized recursive maximum correntropy (CR-RMC)
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