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International Symposium on Neural Networks

ISNN 2019: Advances in Neural Networks – ISNN 2019 pp 48–57Cite as

Unsupervised Feature Selection Using RBF Autoencoder

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11554))

Abstract

In this paper, a novel learning approach to solve unsupervised feature selection in high-dimensional data is proposed, namely Radial Basis Function Autoencoder feature selection (RAFS). This method based on autoencoder uses the radial basis function to achieve mapping instead of the weight. We also consider penalty to give a powerful constraint on redundant features. In extensive experiments, our method shows its outperformance in fair comparison with several other methods.

This work was supported in part by the National Natural Science Foundation of China under Grant 6130507, in part by the Natural Science Foundation of Shandong Province under Grant ZR2015AL014 and Grant ZR201709220208, and in part by the Fundamental Research Funds for the Central Universities under Grant 15CX08011A and Grant 18CX02036A.

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Author information

Authors and Affiliations

  1. College of Science, China University of Petroleum, Qingdao, China

    Ling Yu, Zhen Zhang, Xuetao Xie, Hua Chen & Jian Wang

Authors
  1. Ling Yu
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  2. Zhen Zhang
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  3. Xuetao Xie
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  4. Hua Chen
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  5. Jian Wang
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Corresponding author

Correspondence to Jian Wang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. Sichuan University, Chengdu, China

    Huajin Tang

  3. Northeastern University, Shenyang, China

    Zhanshan Wang

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Yu, L., Zhang, Z., Xie, X., Chen, H., Wang, J. (2019). Unsupervised Feature Selection Using RBF Autoencoder. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-22796-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22795-1

  • Online ISBN: 978-3-030-22796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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