Adaptive Learning Methods for Nonlinear System Modeling

Chapter 7

A Random Fourier Features Perspective of KAFs With Application to Distributed Learning Over Networks

Pantelis Bouboulis^⁎; Sergios Theodoridis^⁎; Symeon Chouvardas^{†    ⁎}National and Kapodistrian University of Athens, Athens, Greece^†Capital Fund Management, Paris, France

Abstract

A major problem in any typical online kernel-based scheme is that the model's solution is given as an expansion of kernel functions that grows linearly with time. Usually, some sort of pruning strategy is adopted to make the solution sparse for practical reasons. The key idea is to keep the most informative training data in the expansion (the so-called dictionary), while the rest is omitted. Although these strategies have been proven effective, they still ...