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Graph Regularized Sparse Autoencoders with Nonnegativity Constraints

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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.

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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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Authors and Affiliations

  1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, P. R. China

    Yueyang Teng, Yichao Liu, Jinliang Yang, Chen Li, Shouliang Qi & Yan Kang

  2. Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

    Fenglei Fan & Ge Wang

Authors
  1. Yueyang Teng
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  2. Yichao Liu
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  3. Jinliang Yang
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  4. Chen Li
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  6. Yan Kang
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  7. Fenglei Fan
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  8. Ge Wang
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Correspondence to Yueyang Teng.

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Teng, Y., Liu, Y., Yang, J. et al. Graph Regularized Sparse Autoencoders with Nonnegativity Constraints. Neural Process Lett 50, 247–262 (2019). https://doi.org/10.1007/s11063-019-10039-3

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  • DOI: https://doi.org/10.1007/s11063-019-10039-3

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