A distributed algorithm for graph semi-supervised learning

•A distributed algorithm is proposed to solve graph semi-supervised learning problem by leveraging the graph topology.•The convergence of the distributed algorithm is explicitly proved.•Numerical results verify the good performance and low cost of the distributed algorithm. Graph semi-supervised lea...

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Veröffentlicht in:	Pattern recognition letters Jg. 151; S. 48 - 54
Hauptverfasser:	Huang, Daxin, Jiang, Junzheng, Zhou, Fang, Ouyang, Shan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.11.2021 Elsevier Science Ltd
Schlagworte:	Algorithms Computational geometry Computer applications Convexity Distributed algorithm Graph semi-supervised learning (GSSL) Graph signal processing Laplacian Machine learning Regularization Semi-supervised learning Topology optimization Graph semi-supervised learning (GSSL) Distributed algorithm 41A10 65D05 65D17 Graph signal processing 41A05 Laplacian
ISSN:	0167-8655, 1872-7344
Online-Zugang:	Volltext
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