Um algoritmo kernel baseado na ortogonalização de Gram-Schmidt para redes de difusão adaptativas
André Amaro Bueno, Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva. Um algoritmo kernel baseado na ortogonalização de Gram-Schmidt para redes de difusão adaptativas. Simpósio Brasileiro de Telecomunicações – SBrT’2021, 2021, Online Conference. Anais do SBrT 2021, 2021, pp.1–5. (available only in portuguese)
Adaptive diffusion networks have attracted attention in the scientiﬁc community as an efﬁcient solution for distributed estimation of signals. They also have been employed in nonlinear estimation problems with distributed kernel adaptive algorithms. These solutions present a high computational cost due to the dictionary of kernel algorithms. In this paper, we propose a reduced-cost kernel algorithm for adaptive diffusion networks. It is based on the Gram-Schmidt orthogonalization process, used to span the vector space of the mapped vectors contained in the dictionary, which leads to a dictionary with a reduced cardinality. By means of simulations, we observe an advantageous computational savings in comparison to classical techniques.
Adaptive diffusion networks, kernel adaptive ﬁltering, dictionary sparsiﬁcation, nonlinear signal processing.