Projected Kernel Recursive Maximum Correntropy

In this brief, a different kernel recursive maximum correntropy algorithm is derived using the weighted output information, called KRMC-W. To curb the network growth, we propose a new online sparsification strategy in a feature space, named vector projection (VP) method. Applying the VP to KRMC-W, two novel sparsified kernel adaptive filters, called soft projected KRMC-W and hard projected KRMC-W, respectively, are developed to reduce the average computation complexity. Simulation results under the case of <inline-formula> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula>-stable noise are conducted to demonstrate the efficiencies of the proposed algorithms.

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