Multi-output kernel adaptive filtering with reduced complexity

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URI: http://hdl.handle.net/10902/23800
ISBN: 978-1-7281-5768-9
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Autoría
Cuevas Fernández, DiegoAutoridad Unican; Santamaría Caballero, Luis IgnacioAutoridad Unican
Fecha
2021
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publicado en
IEEE Statistical Signal Processing Workshop (SSP), Río de Janeiro, Brazil, 2021, 306-310
Editorial
Institute of Electrical and Electronics Engineers, Inc.
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Palabras clave
Multi-input multi-output (MIMO) regression
Kernel adaptive filtering
Quantized Kernel Least Mean Square (QKLMS)
Random Fourier features
Resumen/Abstract
In this paper, two new multi-output kernel adaptive filtering algorithms are developed that exploit the temporal and spatial correlations among the input-output multivariate time series. They are multi-output versions of the popular kernel least mean squares (KLMS) algorithm with two different sparsification criteria. The first one, denoted as MO-QKLMS, uses the coherence criterion in order to limit the dictionary size. The second one, denoted as MO-RFF-KLMS, uses random Fourier features (RFF) to approximate the kernel functions by linear inner products. Simulation results with synthetic and real data are presented to assess convergence speed, steady-state performance and complexities of the proposed algorithms.
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