Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel approach using multiple kernels to nonlinear adaptive filtering problems. We present two types of multi-kernel adaptive filtering algorithms, both of which are based on the kernel normalized least mean square (KNLMS) algorithm (Richard et al., 2009). One is a simple generalization of KNLMS, adopting the coherence criterion for dictionary selection. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function penalized by a weighted block ℓ1 norm. The latter algorithm operates the weighted block soft-thresholding which encourages the sparsity of dictionary at the block level. Numerical examples demonstrate the efficacy of the proposed approach. |
Year | Venue | Keywords |
|---|---|---|
2011 | Barcelona | adaptive filters,least mean squares methods,nonlinear filters,operating system kernels,knlms,adaptive proximal forward-backward splitting method,coherence criterion,dictionary selection,kernel normalized least mean square,multikernel adaptive filtering algorithm,multiple kernel,nonlinear adaptive filtering technique,squared distance function,weighted block soft-thresholding,approximation algorithms,vectors,memory management,kernel,dictionaries |
Field | DocType | ISSN |
Least mean squares filter,Kernel (linear algebra),Approximation algorithm,Square (algebra),Pattern recognition,Metric (mathematics),Coherence (physics),Artificial intelligence,Kernel adaptive filter,Variable kernel density estimation,Mathematics | Conference | 2076-1465 |
Citations | PageRank | References |
8 | 0.57 | 5 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Yukawa | 1 | 272 | 30.44 |