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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Vydáno v:Pattern recognition letters Ročník 151; s. 48 - 54
Hlavní autoři: Huang, Daxin, Jiang, Junzheng, Zhou, Fang, Ouyang, Shan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.11.2021
Elsevier Science Ltd
Témata:
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
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Shrnutí:•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 learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex optimization with Laplacian-based regularization. However, the current algorithm to solve the problem is centralized approach calling for heavy computational cost, particularly when the data is of large volume. In this paper, an innovate distributed algorithm is proposed to solve the problem, which is based on the decomposition of the similar graph. Contrary to the centralized approach, the distributed algorithm only requires the neighboring information for solving the optimization. It is proved that difference between the solutions of the distributed algorithm and centralized counterpart is upper bounded. We apply the proposed algorithm to both the synthetic and real-world datasets. The numerical results verify the effectiveness of the proposed distributed algorithm.
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ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2021.07.021