Amostragem adaptativa aplicada a um algoritmo difuso voltado a grafos
Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva. Amostragem adaptativa aplicada a um algoritmo difuso voltado a grafos. In: Simpósio Brasileiro de Telecomunicações – SBrT’2019, 2019, Petrópolis. Anais do SBrT 2019, 2019, pp.1–5 (available only in portuguese).
Graph signal processing has attracted attention in the signal processing community, since it is an effective tool to deal with large quantities of interrelated data. Recently, a diffusion algorithm for graph adaptive filtering was proposed. However, it suffers from high computational cost since all nodes in the graph are sampled even in steady state. In this paper, we propose an adaptive sampling method for this solution that reduces the computational cost in steady state, while maintaining convergence rate and steady-state performance. We also present an analysis to give insights about proper choices for its daptation parameters.
Graph signal processing, sampling on graphs, diffusion strategies, graph filtering, convex combination.