Fast Gaussian Process Regression using KD-trees

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. Authors: Yirong Shen, Andrew Y. Ng, Matthias Seeger (2006)
AUTHORED BY
Yirong Shen
Andrew Y. Ng
Matthias Seeger

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.

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