From Fixed to Adaptive Budget Robust Kernel Adaptive Filtering

Book excerpt: Indeed the issue is how to deal with the trade-off between system complexity and accuracy performance, and an information learning criterion called Minimal Description Length (MDL) is introduced to kernel adaptive filtering. Two formulations of MDL: batch and online model are developed and illustrated by approximation level selection in KRLS-ALD and center dictionary selection in KLMS respectively. The end result is a methodology that controls the kernel adaptive filter dictionary (model order) according to the complexity of the true system and the input signal for online learning even in nonstationary environments.