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An Incremental Deep Learning Network for On-line Unsupervised Feature Extraction

  • Yu Liang
  • Yi Yang
  • Furao ShenEmail author
  • Jinxi Zhao
  • Tao Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

In this paper, we propose an incremental deep learning network for on-line unsupervised feature extraction. This deep learning network is based on 3 data processing components: (1) cascaded incremental orthogonal component analysis network (IOCANet); (2) binary hashing; and (3) blockwise histograms. In this architecture, IOCANet can process online data and get filters to do convolutions. Binary hashing is used to enhance the nonlinearity of IOCANet and reduce the quantity of the data. Eventually, the data is encoded by blockwise histograms. Experiments demonstrate that the proposed architecture has potential results for on-line unsupervised feature extraction.

Keywords

Deep learning On-line unsupervised feature extraction 

Notes

Acknowledgments

This work is supported in part by the National Science Foundation of China under Grant Nos. (61373130, 61375064, 61373001), and Jiangsu NSF grant (BK20141319).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yu Liang
    • 1
  • Yi Yang
    • 1
  • Furao Shen
    • 1
    Email author
  • Jinxi Zhao
    • 1
  • Tao Zhu
    • 1
  1. 1.National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing UniversityNanjingChina

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