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Multi-view Deep Gaussian Processes

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11301))

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Abstract

Deep Gaussian processes (DGPs) have shown their power in many tasks of machine learning. However, when they deal with multi-view data, DGPs assume the same modeling depth for different views of data, which is quite unreasonable because there are usually large diversities among different views. In this paper, we propose the model of multi-view deep Gaussian processes (MvDGPs), which takes full account of the characteristics of multi-view data. Combining the advantages of the DGPs with the multi-view learning, MvDGPs can independently determine the modeling depths for each view, which is more flexible and powerful. In contrast with the DGPs, MvDGPs support asymmetrical modeling depths for different view of data, resulting in better characterizations of the discrepancies among different views. Experimental results on multiple multi-view data sets have verified the flexibilities and effectiveness of the proposed model.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Project 61673179, and Shanghai Knowledge Service Platform Project (No. ZF1213).

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Authors and Affiliations

  1. Department of Computer Science and Technology, East China Normal University, 3663, North Zhongshan Road, Shanghai, 200062, China

    Shiliang Sun & Qiuyang Liu

Authors
  1. Shiliang Sun
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  2. Qiuyang Liu
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Corresponding author

Correspondence to Shiliang Sun .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Sun, S., Liu, Q. (2018). Multi-view Deep Gaussian Processes. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-04167-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04166-3

  • Online ISBN: 978-3-030-04167-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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