Nonlinear adaptive filtering techniques with multiple kernels

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)136-140
Number of pages5
JournalEuropean Signal Processing Conference
Publication statusPublished - 2011 Dec 1
Externally publishedYes
Event19th European Signal Processing Conference, EUSIPCO 2011 - Barcelona, Spain
Duration: 2011 Aug 292011 Sept 2

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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