Data Selection Kernel Conjugate Gradient Algorithm

In recent years, the interest in kernel methods has increased exponentially, mainly due to applications including phenomena that cannot be well modeled by linear systems. Furthermore, the demand for high-speed communications and improvement in computer capacity to process information leads to the exploration of more sophisticated resources. The kernel adaptive filtering is an alternative to deal with nonlinear problems. In this paper, we propose the data selection kernel conjugate gradient (DS-KCG) algorithm, which is capable of classifying whether the currently available data brings sufficient innovation to update the filter coefficients. The data could be discarded, avoiding extra computation and performance degradation.