Name
Abstract | ||
|---|---|---|
This paper exemplifies that the use of multiple kernels leads to efficient adaptive filtering for nonlinear systems. Two types of multikernel adaptive filtering algorithms are proposed. One is a simple generalization of the kernel normalized least mean square (KNLMS) algorithm , adopting a coherence criterion for dictionary designing. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function plus a weighted block $\\ell_{1}$ norm penalty, encouraging the sparsity of an adaptive filter at the block level for efficiency. The proposed multikernel approach enjoys a higher degree of freedom than those approaches which design a kernel as a convex combination of multiple kernels. Numerical examples show that the proposed approach achieves significant gains particularly for nonstationary data as well as insensitivity to the choice of some design-parameters. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TSP.2012.2200889 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
reproducing kernel hilbert space,algorithm design and analysis,memory management,adaptive filters,computational complexity,vectors,kernel,dictionaries | Least mean squares filter,Mathematical optimization,Kernel embedding of distributions,Convex combination,Kernel principal component analysis,Multikernel,Adaptive filter,Kernel adaptive filter,Variable kernel density estimation,Mathematics | Journal |
Volume | Issue | ISSN |
60 | 9 | 1053-587X |
Citations | PageRank | References |
10 | 0.54 | 33 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Yukawa | 1 | 272 | 30.44 |