Abstract
We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain ℓ1-ball. In the second step, it identifies the exact support by detailed examinations based on an ℓ2-ball. Also, we employ the identified support given by the second step for coherence construction. The proposed method reduces the amount of ℓ2-distance evaluations, leading to the complexity reduction. The numerical examples show that the proposed algorithm achieves significant complexity reduction.
| Original language | English |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3367-3371 |
| Number of pages | 5 |
| Volume | 2015-August |
| ISBN (Print) | 9781467369978 |
| DOIs | |
| Publication status | Published - 2015 Aug 4 |
| Event | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia Duration: 2014 Apr 19 → 2014 Apr 24 |
Other
| Other | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 |
|---|---|
| Country | Australia |
| City | Brisbane |
| Period | 14/4/19 → 14/4/24 |
Keywords
- Compactly supported function
- Gaussian kernel
- Kernel learning
- Positive definite function
- Radial basis function
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering
Cite this
An efficient kernel normalized least mean square algorithm with compactly supported kernel. / Toda, Osamu; Yukawa, Masahiro.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2015-August Institute of Electrical and Electronics Engineers Inc., 2015. p. 3367-3371 7178595.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An efficient kernel normalized least mean square algorithm with compactly supported kernel
AU - Toda, Osamu
AU - Yukawa, Masahiro
PY - 2015/8/4
Y1 - 2015/8/4
N2 - We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain ℓ1-ball. In the second step, it identifies the exact support by detailed examinations based on an ℓ2-ball. Also, we employ the identified support given by the second step for coherence construction. The proposed method reduces the amount of ℓ2-distance evaluations, leading to the complexity reduction. The numerical examples show that the proposed algorithm achieves significant complexity reduction.
AB - We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain ℓ1-ball. In the second step, it identifies the exact support by detailed examinations based on an ℓ2-ball. Also, we employ the identified support given by the second step for coherence construction. The proposed method reduces the amount of ℓ2-distance evaluations, leading to the complexity reduction. The numerical examples show that the proposed algorithm achieves significant complexity reduction.
KW - Compactly supported function
KW - Gaussian kernel
KW - Kernel learning
KW - Positive definite function
KW - Radial basis function
UR - http://www.scopus.com/inward/record.url?scp=84946045881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946045881&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178595
DO - 10.1109/ICASSP.2015.7178595
M3 - Conference contribution
AN - SCOPUS:84946045881
SN - 9781467369978
VL - 2015-August
SP - 3367
EP - 3371
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -