Kernel adaptive filter

A Kernel Adaptive Filter (KAF) is a type of adaptive filtering technique that utilizes kernel methods to deal with nonlinear problems. Traditional adaptive filters, like the Least Mean Squares (LMS) or Recursive Least Squares (RLS), generally work well for linear systems but struggle in the presence of nonlinearities in the data or signal characteristics. The main idea behind kernel adaptive filters is to use a kernel function to map the input data into a higher-dimensional feature space where linear relations can be learned more effectively.