TY - JOUR
T1 - Nonlinear adaptive filtering techniques with multiple kernels
AU - Yukawa, Masahiro
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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 ℓ 1 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.
AB - 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 ℓ 1 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.
UR - http://www.scopus.com/inward/record.url?scp=84863761905&partnerID=8YFLogxK
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M3 - Conference article
AN - SCOPUS:84863761905
SP - 136
EP - 140
JO - European Signal Processing Conference
JF - European Signal Processing Conference
SN - 2219-5491
T2 - 19th European Signal Processing Conference, EUSIPCO 2011
Y2 - 29 August 2011 through 2 September 2011
ER -