A Matching Pursuit Approach to Sparse Gaussian Process Regression
Abstract
In this paper we propose a new basis selection criterion for building sparse GP regression models that provides promising gains in accuracy as well as efficiency over previous methods. Our algorithm is much faster than that of Smola and Bartlett, while, in generalization it greatly outperforms the information gain approach proposed by Seeger et al, especially on the quality of predictive distributions.
Cite
Text
Keerthi and Chu. "A Matching Pursuit Approach to Sparse Gaussian Process Regression." Neural Information Processing Systems, 2005.Markdown
[Keerthi and Chu. "A Matching Pursuit Approach to Sparse Gaussian Process Regression." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/keerthi2005neurips-matching/)BibTeX
@inproceedings{keerthi2005neurips-matching,
title = {{A Matching Pursuit Approach to Sparse Gaussian Process Regression}},
author = {Keerthi, Sathiya and Chu, Wei},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {643-650},
url = {https://mlanthology.org/neurips/2005/keerthi2005neurips-matching/}
}