Online learning based on iterative projections in sum space of linear and Gaussian reproducing kernel Hilbert spaces

We propose a novel multikernel adaptive filtering algorithm based on the iterative projections in the sum space of reproducing kernel Hilbert spaces. We employ linear and Gaussian kernels, envisioning an application to partially-linear-system identification/estimation. The algorithm is derived by re...

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Veröffentlicht in:2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) S. 3362 - 3366
1. Verfasser: Yukawa, Masahiro
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2015
Schlagworte:
Adaptation models
Complexity theory
Dictionaries
Hilbert space
Kernel
Manganese
multikernel adaptive filtering
orthogonal projection
reproducing kernel Hilbert space
Signal processing algorithms
sum space
ISSN:1520-6149
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Zusammenfassung:We propose a novel multikernel adaptive filtering algorithm based on the iterative projections in the sum space of reproducing kernel Hilbert spaces. We employ linear and Gaussian kernels, envisioning an application to partially-linear-system identification/estimation. The algorithm is derived by reformulating the hyperplane projection along affine subspace (HYPASS) algorithm in the sum space. The projection is computable by virtue of Minh's theorem proved in 2010 as long as the input space has nonempty interior. Numerical examples show the efficacy of the proposed algorithm.
ISSN:1520-6149
DOI:10.1109/ICASSP.2015.7178594