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An Improved Supervised Isomap Method Using Adaptive Parameters

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Intelligent Computing Methodologies (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10956))

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Abstract

In this paper, to deal with the problem that nearest neighbor graph is hard to be connected in the original Isomap, a new supervised Isomap method (SS-Isomap) with adaptive parameters is proposed. This method considers the density of intra-class data points and proposes an adaptive function. The experiment results based on UCI datasets show that SS-Isomap has a better discriminant ability.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant no. 61273303 and no. 61572381). The authors would like to thank all the editors and reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Sciences and Technology, Wuhan, 430065, China

    Zhongkai Feng & Bo Li

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Sciences and Technology, Wuhan, 430065, China

    Zhongkai Feng & Bo Li

Authors
  1. Zhongkai Feng
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  2. Bo Li
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Corresponding author

Correspondence to Zhongkai Feng .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Feng, Z., Li, B. (2018). An Improved Supervised Isomap Method Using Adaptive Parameters. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_27

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95956-6

  • Online ISBN: 978-3-319-95957-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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