no code implementations • 26 Mar 2024 • Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
In particular, we consider in our theoretical analysis the diffusion least-mean-squares algorithm in a scenario in which the nodes are randomly sampled.
no code implementations • 5 Aug 2020 • Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area.
no code implementations • 13 Jul 2020 • Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva
In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node.
