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The maximum correntropy criterion (MCC) has been employed to designoutlier-robust adaptive filtering algorithms, among which the recursive MCC(RMCC) algorithm is a typical one. Motivated by the success of our recentlyproposed proportionate recursive least squares (PRLS) algorithm for sparsesystem identification, we propose to introduce the proportionate updating (PU)mechanism into the RMCC, leading to two sparsity-aware RMCC algorithms: theproportionate recursive MCC (PRMCC) algorithm and the combinational PRMCC(CPRMCC) algorithm. The CPRMCC is implemented as an adaptive convex combinationof two PRMCC filters. For PRMCC, its stability condition and mean-squareperformance were analyzed. Based on the analysis, optimal parameter selectionin nonstationary environments was obtained. Performance study of CPRMCC wasalso provided and showed that the CPRMCC performs at least as well as thebetter component PRMCC filter in steady state. Numerical simulations of sparsesystem identification corroborate the advantage of proposed algorithms as wellas the validity of theoretical analysis.

Proportionate Recursive Maximum Correntropy Criterion Adaptive Filtering Algorithms and their Performance Analysis