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 |
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| Jazyk: | angličtina |
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Elsevier B.V
01.11.2021
Elsevier Science Ltd |
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | •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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| AbstractList | •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. 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. |
| Author | Jiang, Junzheng Ouyang, Shan Huang, Daxin Zhou, Fang |
| Author_xml | – sequence: 1 givenname: Daxin surname: Huang fullname: Huang, Daxin organization: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China – sequence: 2 givenname: Junzheng surname: Jiang fullname: Jiang, Junzheng organization: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China – sequence: 3 givenname: Fang surname: Zhou fullname: Zhou, Fang email: zhoufang1026@guet.edu.cn organization: School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China – sequence: 4 givenname: Shan surname: Ouyang fullname: Ouyang, Shan organization: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China |
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| Cites_doi | 10.1017/S026988891200001X 10.1109/TNN.2010.2099237 10.1109/TKDE.2018.2810286 10.1023/B:MACH.0000033120.25363.1e 10.1109/TCYB.2017.2703610 10.1162/089976600300014980 10.1145/1883612.1883617 10.1016/j.jclepro.2018.07.164 10.1109/TIP.2017.2717191 10.1007/978-3-319-49787-7_4 10.1109/TSP.2012.2188718 10.1007/s10115-009-0209-z 10.1145/2089125.2089129 10.1109/5.18626 10.1111/j.1469-1809.1936.tb02137.x 10.1109/TSP.2019.2922160 |
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| Keywords | Graph semi-supervised learning (GSSL) Distributed algorithm 41A10 65D05 65D17 Graph signal processing 41A05 Laplacian |
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| Snippet | •A distributed algorithm is proposed to solve graph semi-supervised learning problem by leveraging the graph topology.•The convergence of the distributed... Graph semi-supervised learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex... |
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| SubjectTerms | 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 |
| Title | A distributed algorithm for graph semi-supervised learning |
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