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An Observation Angle Dependent Nonstationary Covariance Function for Gaussian Process Regression

  • Conference paper
Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5863))

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Abstract

Despite the success of Gaussian Processes (GPs) in machine learning, the range of applications and expressiveness of GP models are confined by the limited set of available covariance functions. This paper presents a new non-stationary covariance function which allows simple geometric interpretation and depends on the angle at which points can be seen from an observation centre. The construction of the new covariance function and the proof of its positive semi-definiteness are based on geometric reasoning combined with analytic computations. Experiments conducted with both artificial and real datasets demonstrate the advantages of the developed covariance function.

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Author information

Authors and Affiliations

  1. Australian Centre for Field Robotics, The University of Sydney, NSW, 2006, Australia

    Arman Melkumyan & Eric Nettleton

Authors
  1. Arman Melkumyan
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  2. Eric Nettleton
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Melkumyan, A., Nettleton, E. (2009). An Observation Angle Dependent Nonstationary Covariance Function for Gaussian Process Regression. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_37

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  • DOI: https://doi.org/10.1007/978-3-642-10677-4_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10676-7

  • Online ISBN: 978-3-642-10677-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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