Nonlinear Signal Processing Based on Reproducing Kernel Hilbert Space
Jianwu Xu
Broschiertes Buch

Nonlinear Signal Processing Based on Reproducing Kernel Hilbert Space

Concepts,Methods and Experiments

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Over the last few decades, there has been much research on reproducing kernel Hilbert space (RKHS) for machine learning. This monograph applies RKHS for nonlinear signal processing. It proposes a new statistical descriptor, called correntropy, to characterize the higher order statistical information and nonlinearity intrinsic to random processes. Correntropy and centered correntropy functions can be formulated as "generalized" correlation and covariance functions on nonlinearly transformed random signals via the data independent kernel functions. Those nonlinearly transformed signals appear on...