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Gaussian Process Regression for Structured Data Sets

  • Mikhail Belyaev
  • Evgeny Burnaev
  • Yermek KapushevEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9047)

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.

Keywords

Gaussian process Structured data Regularization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mikhail Belyaev
    • 1
    • 2
    • 3
  • Evgeny Burnaev
    • 1
    • 2
    • 3
  • Yermek Kapushev
    • 1
    • 2
    Email author
  1. 1.Institute for Information Transmission ProblemsMoscowRussia
  2. 2.DATADVANCE, llcMoscowRussia
  3. 3.PreMoLabMIPTDolgoprudnyRussia

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