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Conference Paper

Fast Gaussian Process Regression using KD-Trees

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Shen, Y., Ng, A., & Seeger, M. (2006). Fast Gaussian Process Regression using KD-Trees. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, 1225-1232.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D1E1-4
Abstract
The computation required for Gaussian process regression with n training examples is about O(n3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.