Online learning with kernels: Overcoming the growing sum problem
Online kernel algorithms have an important computational drawback. The computational complexity of these algorithms grow linearly over time. This makes these algorithms difficult to use for real time signal processing applications that need to continuously process data over prolonged periods of time. In this paper, we present a way of overcoming this problem. We do so by approximating kernel evaluations using finite dimensional inner products in a randomized feature space. We apply this idea to the Kernel Least Mean Square (KLMS) algorithm, that has recently been proposed as a non-linear extension to the famed LMS algorithm. Our simulations show that using the proposed method, constant computational complexity can be achieved, with no observable loss in performance.