@conference {604,
	title = {Multi-output kernel adaptive filtering with reduced complexity},
	booktitle = {IEEE Statistical Signal Processing Workshop},
	year = {2021},
	month = {July},
	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 MORFF-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.},
	author = {Cuevas, Diego and Santamar{\'\i}a, Ignacio}
}
