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Graph Regularized Sparse Autoencoders with Nonnegativity Constraints

  • Yueyang TengEmail author
  • Yichao Liu
  • Jinliang Yang
  • Chen Li
  • Shouliang Qi
  • Yan Kang
  • Fenglei Fan
  • Ge Wang
Article
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Abstract

Unsupervised feature learning with deep networks has been widely studied in recent years. Among these networks, deep autoencoders have shown a decent performance in discovering hidden geometric structure of the original data. Both nonnegativity and graph constraints show the effectiveness in representing intrinsic structures in the high dimensional ambient space. This paper combines the nonnegativity and graph constraints to find the original geometrical information intrinsic to high dimensional data, keeping it in a dimensionality reduced space. In the experiments, we test the proposed networks on several standard image data sets. The results demonstrate that they outperform existing methods.

Keywords

Autoencoder Deep network Graph regularization Part-based representation Unsupervised learning 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundations of China (81671773), Fundamental Research Funds for the Central Universities (N171903004) and Natural Science Foundation of Liaoning Province of China (20170540321).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yueyang Teng
    • 1
    Email author
  • Yichao Liu
    • 1
  • Jinliang Yang
    • 1
  • Chen Li
    • 1
  • Shouliang Qi
    • 1
  • Yan Kang
    • 1
  • Fenglei Fan
    • 2
  • Ge Wang
    • 2
  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangP. R. China
  2. 2.Department of Biomedical EngineeringRensselaer Polytechnic InstituteTroyUSA

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