Regularized Kernel Recursive Least Square Algoirthm
Abstract
In most adaptive signal processing applications, system linearity is assumed and adaptive linear filters are thus used. The traditional class of supervised adaptive filters rely on error-correction learning for their adaptive capability. The kernel method is a powerful nonparametric modeling tool for pattern analysis and statistical signal processing. Through a nonlinear mapping, kernel methods transform the data into a set of points in a Reproducing Kernel Hilbert Space. KRLS achieves high accuracy and has fast convergence rate in stationary scenario. However the good performance is obtained at a cost of high computation complexity. Sparsification in kernel methods is know to related to less computational complexity and memory consumption.
Cite
@article{arxiv.1508.07103,
title = {Regularized Kernel Recursive Least Square Algoirthm},
author = {Songlin Zhao},
journal= {arXiv preprint arXiv:1508.07103},
year = {2015}
}