@conference{10902/23800,
year = {2021},
url = {http://hdl.handle.net/10902/23800},
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.},
organization = {This work was supported by the Ministerio de Ciencia, Innovación y  Universidades and AEI/FEDER funds of the E.U., under grant PID2019-104958RB-C43 (ADELE).},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE Statistical Signal Processing Workshop (SSP), Río de Janeiro, Brazil, 2021, 306-310},
title = {Multi-output kernel adaptive filtering with reduced complexity},
author = {Cuevas Fernández, Diego and Santamaría Caballero, Luis Ignacio},
}