TY - GEN
T1 - An efficient kernel normalized least mean square algorithm with compactly supported kernel
AU - Toda, Osamu
AU - Yukawa, Masahiro
N1 - Publisher Copyright:
© 2015 IEEE.
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
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U2 - 10.1109/ICASSP.2015.7178595
DO - 10.1109/ICASSP.2015.7178595
M3 - Conference contribution
AN - SCOPUS:84946045881
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3367
EP - 3371
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Y2 - 19 April 2014 through 24 April 2014
ER -