Ensemble Gaussian Processes for Online Learning Over Graphs With Adaptivity and Scalability

In the past decade, semi-supervised learning (SSL) over graphs has gained popularity due to its importance in a gamut of network science applications. While most of existing SSL methods provide only point estimates of the targeted variables, the present work capitalizes on Gaussian processes (GPs) t...

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Vydané v:	IEEE transactions on signal processing Ročník 70; s. 17 - 30
Hlavní autori:	Polyzos, Konstantinos D., Lu, Qin, Giannakis, Georgios B.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Connectivity Covariance matrices Distance learning ensemble learning Gaussian process Gaussian processes Graphs Kernel Machine learning Mathematical analysis Nodes online learning Radio frequency regret analysis Safety critical Scalability semi-supervised learning over graphs Semisupervised learning Task analysis Uncertainty
ISSN:	1053-587X, 1941-0476
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