Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information

We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph s...

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Vydané v:	Algorithms Ročník 13; číslo 9; s. 228
Hlavní autori:	Shafipour, Rasoul, Mateos, Gonzalo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.09.2020
Predmet:	Algorithms Covariance matrix graph signal processing Inference Inverse problems Machine learning Network topologies network topology inference online optimization proximal gradient algorithm Signal processing streaming data
ISSN:	1999-4893, 1999-4893
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