Abstract
Graph Convolutional Networks (GCNs) recently have been adopted in several feature representation studies for different classification tasks. While many of these methods are used to work with irregular structure data, they are rarely used to learn regular structure data. It is crucial to construct an excellent graph representation to traditional classification tasks for obtaining the sufficient data representation, including attribute representation and relative representation. In this context, we propose a novel method to construct a reasonable graph representation by capturing the relations in low dimensional space among the data. In order to get the well-represented, we introduce the low-rank constraint and L2-norm regularization to the graph learning framework simultaneously. Experiments demonstrate that the proposed method to learn graph representation is helpful for classification task, and leads to improved performance when compared to state-of-the-art graph learning methods on twelve data sets.
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Acknowledgements
This work is supported by Innovation Project of Guangxi Graduate Education, Basic Competence Promotion Project for Young and Middle-aged Teachers in Guangxi Education Department (No. 2017KY0176), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (No. MIMS19-M-02), Key Laboratory of Software Engineering in Guangxi University for Nationalities (No.2019-18XJSY-03), National Natural Science Foundation of China under Grant No. 62062011, Guangxi Natural Science Foundation under Grant No. 2017GXNSFAA198008 and Open Fund Grant No. GXIC20-06 of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis.
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Beixian Zhang and Meiling Liu had equivalent contribution to this work.
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Zhang, B., Liu, M., Zhou, B. et al. Graph learning in low dimensional space for graph convolutional networks. Multimed Tools Appl 81, 34263–34279 (2022). https://doi.org/10.1007/s11042-021-11033-5
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DOI: https://doi.org/10.1007/s11042-021-11033-5