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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© 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
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