Name
Title | ||
|---|---|---|
An efficient kernel adaptive filtering algorithm using hyperplane projection along affine subspace |
Abstract | ||
|---|---|---|
We propose a novel kernel adaptive filtering algorithm that selectively updates a few coefficients at each iteration by projecting the current filter onto the zero instantaneous-error hyperplane along a certain time-dependent affine subspace. Coherence is exploited for selecting the coefficients to be updated as well as for measuring the novelty of new data. The proposed algorithm is a natural extension of the normalized kernel least mean squares algorithm operating iterative hyperplane projections in a reproducing kernel Hilbert space. The proposed algorithm enjoys low computational complexity. Numerical examples indicate high potential of the proposed algorithm. |
Year | Venue | Keywords |
|---|---|---|
2012 | Signal Processing Conference | adaptive filters,computational complexity,iterative methods,least mean squares methods,affine subspace,computational complexity,efficient Kernel adaptive filtering algorithm,hyperplane projection,iteration,kernel Hilbert space,natural extension,normalized kernel least mean squares algorithm,time-dependent affine subspace,zero instantaneous-error hyperplane,kernel adaptive filter,normalized kernel least mean square algorithm,projection algorithms,reproducing kernel Hilbert space |
Field | DocType | ISSN |
Mathematical optimization,Radial basis function kernel,Kernel embedding of distributions,Algorithm,Kernel principal component analysis,Polynomial kernel,Kernel adaptive filter,Hyperplane,Variable kernel density estimation,Mathematics,Kernel (statistics) | Conference | 2219-5491 |
ISBN | Citations | PageRank |
978-1-4673-1068-0 | 8 | 0.54 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Yukawa | 1 | 272 | 30.44 |
| Ryu-ichiro Ishii | 2 | 8 | 0.54 |