Fast Gaussian Process Regression Using KD-Trees

The computation required for Gaussian process regression with n training examples is about O(n^3) 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.


Published in:
Proceedings of the 19th Annual Conference on Neural Information Processing Systems
Presented at:
Neural Information Processing Systems 18, Vancouver, BC
Year:
2006
Keywords:
Laboratories:




 Record created 2010-12-01, last modified 2018-09-25

n/a:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)