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Multikernel Least Mean Square Algorithm

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3 Author(s)
Felipe A. Tobar ; Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK ; Sun-Yuan Kung ; Danilo P. Mandic

The multikernel least-mean-square algorithm is introduced for adaptive estimation of vector-valued nonlinear and nonstationary signals. This is achieved by mapping the multivariate input data to a Hilbert space of time-varying vector-valued functions, whose inner products (kernels) are combined in an online fashion. The proposed algorithm is equipped with novel adaptive sparsification criteria ensuring a finite dictionary, and is computationally efficient and suitable for nonstationary environments. We also show the ability of the proposed vector-valued reproducing kernel Hilbert space to serve as a feature space for the class of multikernel least-squares algorithms. The benefits of adaptive multikernel (MK) estimation algorithms are illuminated in the nonlinear multivariate adaptive prediction setting. Simulations on nonlinear inertial body sensor signals and nonstationary real-world wind signals of low, medium, and high dynamic regimes support the approach.

Published in:

IEEE Transactions on Neural Networks and Learning Systems  (Volume:25 ,  Issue: 2 )