2017/08/27 by Rodrigo C. de Lamare, de Lamare, R. C., André R. Flores +1
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Speech and Audio Processing
paper · pdf · doi:10.48550/arxiv.1708.08142
openalex publication_date 2017/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of new inputs. In this paper we derive the set-membership kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is capable of limiting the size of the dictionary created in stationary environments. We also derive as an extension the set-membership kernelized affine projection (SM-KAP) algorithm. Finally several experiments are presented to compare the proposed SM-NKLMS and SM-KAP algorithms to the existing methods.