Bounded Gaussian Process Regression

Sand Jensen, B., Nielsen, J. B. and Larsen, J. (2013) Bounded Gaussian Process Regression. In: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Southampton, UK, 22-25 Sept 2013, pp. 1-6. ISBN 9781479911806 (doi:10.1109/MLSP.2013.6661916)

Sand Jensen, B., Nielsen, J. B. and Larsen, J. (2013) Bounded Gaussian Process Regression. In: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Southampton, UK, 22-25 Sept 2013, pp. 1-6. ISBN 9781479911806 (doi:10.1109/MLSP.2013.6661916)

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Abstract

We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We approximate the intractable posterior distributions by the Laplace approximation and expectation propagation and show the properties of the models on an artificial example. We finally consider two real-world data sets originating from perceptual rating experiments which indicate a significant gain obtained with the proposed explicit noise-model extension.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jensen, Dr Bjorn Sand
Authors: Sand Jensen, B., Nielsen, J. B., and Larsen, J.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781479911806
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