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Component Random Walk

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Emerging Intelligent Computing Technology and Applications (ICIC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 304))

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Abstract

Label propagation has become a successful method for transductive learning. In this paper, we propose a unified label propagation model named Component Random Walk. We demonstrate that besides most of the existing label propagation algorithms, a novel Multilevel Component Propagation (MCP) algorithm can be derived from this Component Random Walk model as well. Promising experimental results are provided for MCP algorithm.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Yangzhou University, Yangzhou, 225009, China

    Xiaohua Xu, Ping He, Lin Lu, Zhoujin Pan & Ling Chen

Authors
  1. Xiaohua Xu
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  2. Ping He
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  3. Lin Lu
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  4. Zhoujin Pan
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  5. Ling Chen
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Xu, X., He, P., Lu, L., Pan, Z., Chen, L. (2012). Component Random Walk. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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