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Automatic Model Selection of the Mixtures of Gaussian Processes for Regression

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Advances in Neural Networks – ISNN 2015 (ISNN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

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Abstract

For the learning of mixtures of Gaussian processes, model selection is an important but difficult problem. In this paper, we develop an automatic model selection algorithm for mixtures of Gaussian processes in the light of the reversible jump Markov chain Monte Carlo framework for Gaussian mixtures. In this way, the component number and the parameters are updated according the five types of random moves and model selection can be made automatically. The key idea is that the moves of component splitting or merging preserve the zeroth, first and second moments of the components so that the covariance parameters of the new components can be related to the origin ones. It is demonstrated by the simulation experiments that this automatic model selection algorithm is feasible and effective.

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Author information

Authors and Affiliations

  1. Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China

    Zhe Qiang & Jinwen Ma

Authors
  1. Zhe Qiang
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  2. Jinwen Ma
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Qiang, Z., Ma, J. (2015). Automatic Model Selection of the Mixtures of Gaussian Processes for Regression. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_37

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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