Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6854427 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
adaptive filters,filtering theory,functional analysis,image motion analysis,learning (artificial intelligence),time series,MoCap data set,correntropy-based similarity criterion,functional relevance analysis,functional relevant multichannel kernel adaptive filtering framework,human activity analysis,kernel least mean square filter enhancement,learning model,motion capture data analysis,multichannel time-series,pair-wise channel similarity computation,sample subset extraction,sparseness criteria,MoCap data,adaptive filtering,functional relevance,multichannel data | Relevance analysis,Kernel (linear algebra),Motion capture,Data mining,Pattern recognition,Computer science,Similarity criterion,Communication channel,Artificial intelligence,Adaptive filter,Kernel adaptive filter,Kernel least mean square | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Marino Álvarez-Meza | 1 | 93 | 16.89 |
| German Castellanos-Dominguez | 2 | 258 | 51.21 |
| Jose C. Principe | 3 | 2295 | 282.29 |
| Alvarez-Meza, A. | 4 | 0 | 0.34 |
| Castellanos-Dominguez, G. | 5 | 0 | 0.34 |