Multi-output kernel adaptive filtering with reduced complexity
| Title | Multi-output kernel adaptive filtering with reduced complexity |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Cuevas, D., and I. Santamaría |
| Conference Name | IEEE Statistical Signal Processing Workshop |
| Month Published | 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. |
PDF version:
