Maximum Correntropy Criterion–Based Kernel Adaptive Filters
Badong Chen⁎; Xin Wang⁎; Jose C. Principe⁎,† ⁎Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China†University of Florida, Gainesville, FL, United States
Abstract
Kernel adaptive filters (KAFs) are a family of powerful online kernel learning methods that can learn nonlinear and nonstationary systems efficiently. Most of the existing KAFs are obtained by minimizing the well-known mean square error (MSE). The MSE criterion is computationally simple and very easy to implement but may suffer from a lack of robustness to non-Gaussian noises. To deal with the non-Gaussian noises robustly, the optimization criteria must go beyond the ...
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