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Abstract
This paper investigates the issue of data reduction for noisy data classification in semi-supervised learning. A novel semi-supervised manifold-preserving graph reduction (Semi-MPGR) is proposed for data reduction in the framework of semi-supervised learning. In Semi-MPGR, the adjacent graph consists of three sub-graphs that are constructed by labeled samples, unlabeled ones, and both. In doing so, the role of label information is strengthened. On the basis of the defined graph, Semi-MPGR selects data points according to their connection strength. The retained data could maintain the manifold structure of data and be efficiently handled by semi-supervised classifiers. Experimental results on several real-world data sets indicate the feasibility and validity of Semi-MPGR.
Supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 19KJA550002, the Six Talent Peak Project of Jiangsu Province of China under Grant No. XYDXX-054, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Authors and Affiliations
School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou 215006, Jiangsu, China
Li Zhang, Qingqing Pang, Zhiqiang Xu & Xiaohan Zheng
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, Jiangsu, China
Li Zhang
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- Qingqing Pang
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- Zhiqiang Xu
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- Xiaohan Zheng
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Correspondence toLi Zhang.
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Editors and Affiliations
Department of AI, Ping An Life, Shenzhen, China
Haiqin Yang
Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
Kitsuchart Pasupa
City University of Hong Kong, Kowloon, Hong Kong
Andrew Chi-Sing Leung
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
James T. Kwok
School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Jonathan H. Chan
The Chinese University of Hong Kong, New Territories, Hong Kong
Irwin King
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Zhang, L., Pang, Q., Xu, Z., Zheng, X. (2020). Data Reduction for Noisy Data Classification Using Semi-supervised Manifold-Preserving Graph Reduction. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_34
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