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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Veröffentlicht in:	IEEE transactions on signal processing Jg. 70; S. 17 - 30
Hauptverfasser:	Polyzos, Konstantinos D., Lu, Qin, Giannakis, Georgios B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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