Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS)...

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Vydané v:	IEEE transactions on signal processing Ročník 66; číslo 13; s. 3584 - 3598
Hlavní autori:	Di Lorenzo, Paolo, Banelli, Paolo, Isufi, Elvin, Barbarossa, Sergio, Leus, Geert
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2018
Predmet:	Adaptation and learning Adaptive learning graph signal processing Laplace equations sampling on graphs Signal processing Signal processing algorithms Steady-state successive convex approximation Task analysis
ISSN:	1053-587X, 1941-0476
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