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Unsupervised Feature Selection Using RBF Autoencoder

  • Ling Yu
  • Zhen Zhang
  • Xuetao Xie
  • Hua Chen
  • Jian WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Unsupervised Feature selection Radial basis function Autoencoder Penalty 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ling Yu
    • 1
  • Zhen Zhang
    • 1
  • Xuetao Xie
    • 1
  • Hua Chen
    • 1
  • Jian Wang
    • 1
    Email author
  1. 1.College of ScienceChina University of PetroleumQingdaoChina

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