Quantized Kernel Maximum Correntropy and Its Mean Square Convergence Analysis

Online vector quantization (VQ) method has been successfully applied to the kernel adaptive filters (KAFs) for curbing their linearly growing radial basis function (RBF) network, thereby generating a family of quantized KAFs (QKAFs). However, the most existing QKAFs are based on the mean square error (MSE) criterion, which is actually not a good choice for non-Gaussian signals. In this paper, a new quantized kernel adaptive filter called quantized kernel maximum correntropy (QKMC) is developed,