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Gaussian Process Regression for Structured Data Sets

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Statistical Learning and Data Sciences (SLDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

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Abstract

Approximation algorithms are widely used in many engineering problems. To obtain a data set for approximation a factorial design of experiments is often used. In such case the size of the data set can be very large. Therefore, one of the most popular algorithms for approximation — Gaussian Process regression — can hardly be applied due to its computational complexity. In this paper a new approach for a Gaussian Process regression in case of a factorial design of experiments is proposed. It allows to efficiently compute exact inference and handle large multidimensional and anisotropic data sets.

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Author information

Authors and Affiliations

  1. Institute for Information Transmission Problems, Bolshoy Karetny per. 19, Moscow, 127994, Russia

    Mikhail Belyaev, Evgeny Burnaev & Yermek Kapushev

  2. DATADVANCE, llc, Pokrovsky blvd. 3, Moscow, 109028, Russia

    Mikhail Belyaev, Evgeny Burnaev & Yermek Kapushev

  3. PreMoLab, MIPT, Institutsky per. 9, Dolgoprudny, 141700, Russia

    Mikhail Belyaev & Evgeny Burnaev

Authors
  1. Mikhail Belyaev
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  2. Evgeny Burnaev
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  3. Yermek Kapushev
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Corresponding author

Correspondence to Yermek Kapushev .

Editor information

Editors and Affiliations

  1. University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  3. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

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© 2015 Springer International Publishing Switzerland

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Belyaev, M., Burnaev, E., Kapushev, Y. (2015). Gaussian Process Regression for Structured Data Sets. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-17091-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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