Abstract
We propose a novel algorithm using a reproducing kernel for adaptive nonlinear estimation. The proposed algorithm is based on three ideas: projection-along-subspace, selective update, and parallel projection. The projection-along-subspace yields excellent performances with small dictionary sizes. The selective update effectively reduces the complexity without any serious degradation of performance. The parallel projection leads to fast convergence/tracking accompanied by noise robustness. A convergence analysis in the non-selective-update case is presented by using the adaptive projected subgradient method. Simulation results exemplify the benefits from the three ideas as well as showing the advantages over the state-of-the-art algorithms. The proposed algorithm bridges the quantized kernel least mean square algorithm of Chen and the sparse sequential algorithm of Dodd et al.
Original language | English |
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Article number | 7112637 |
Pages (from-to) | 4257-4269 |
Number of pages | 13 |
Journal | IEEE Transactions on Signal Processing |
Volume | 63 |
Issue number | 16 |
DOIs | |
Publication status | Published - 2015 Aug 15 |
Keywords
- Convex projection
- kernel adaptive filtering
- reproducing kernel Hilbert space
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing