Functional relevant multichannel kernel adaptive filter for human activity analysis

Abstract

A multichannel kernel adaptive filtering framework is presented that highlights relevant channels for the task of analyzing Motion Capture (MoCap) data. Functional relevance analysis is performed over input multichannel data by computing the pair-wise channel similarities to describe the main behavior of the considered applications. Particularly, the well-known Kernel Least Mean Square filter is enhanced using a correntropy-based similarity criterion between channel pairs. Besides, two sparseness criteria are studied to extract a sample subset that constructs a learning model displaying a good trade-off between filter complexity and accuracy. The proposed approach allows devising complex relationship among multi-channel time-series, revealing dependencies among the channels and the process time-structure. The method is tested in a well-known MoCap data set. Results show that our framework is an adequate alternative for finding functional relevance amongst multi-channel time-series.