Approximate Inference in Related Multi-output Gaussian Process Regression

  • Ankit Chiplunkar
  • Emmanuel Rachelson
  • Michele Colombo
  • Joseph Morlier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10163)

Abstract

In Gaussian Processes a multi-output kernel is a covariance function over correlated outputs. Using a prior known relation between outputs, joint auto- and cross-covariance functions can be constructed. Realizations from these joint-covariance functions give outputs that are consistent with the prior relation. One issue with gaussian process regression is efficient inference when scaling upto large datasets. In this paper we use approximate inference techniques upon multi-output kernels enforcing relationships between outputs. Results of the proposed methodology for theoretical data and real world applications are presented. The main contribution of this paper is the application and validation of our methodology on a dataset of real aircraft flight tests, while imposing knowledge of aircraft physics into the model.

Keywords

Gaussian process Kernel methods Approximate inference Multi-output regression Flight-test data 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ankit Chiplunkar
    • 1
    • 3
  • Emmanuel Rachelson
    • 2
  • Michele Colombo
    • 1
  • Joseph Morlier
    • 2
    • 3
  1. 1.Airbus Operations S.A.S.ToulouseFrance
  2. 2.ISAE-Supaero, Département d’Ingénierie des Systèmes Complexes (DISC)ToulouseFrance
  3. 3.Université de Toulouse, CNRS, ISAE-SUPAERO, Institut Clément Ader (ICA)Toulouse Cedex 4France

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