Self-organizing kernel adaptive filtering

Abstract

This paper presents a model-selection strategy based on minimum description length (MDL) that keeps the kernel least-mean-square (KLMS) model tuned to the complexity of the input data. The proposed KLMS-MDL filter adapts its model order as well as its coefficients online, behaving as a self-organizing system and achieving a good compromise between system accuracy and computational complexity without a priori knowledge. Particularly, in a nonstationary scenario, the model order of the proposed algorithm changes continuously with the input data structure. Experiments show the proposed algorithm successfully builds compact kernel adaptive filters with better accuracy than KLMS with sparsity or fixed-budget algorithms.