Learning Gaussian Process Kernels via Hierarchical Bayes
Abstract
We present a novel method for learning with Gaussian process regres- sion in a hierarchical Bayesian framework. In a first step, kernel matri- ces on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystrom method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.
Cite
Text
Schwaighofer et al. "Learning Gaussian Process Kernels via Hierarchical Bayes." Neural Information Processing Systems, 2004.Markdown
[Schwaighofer et al. "Learning Gaussian Process Kernels via Hierarchical Bayes." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/schwaighofer2004neurips-learning/)BibTeX
@inproceedings{schwaighofer2004neurips-learning,
title = {{Learning Gaussian Process Kernels via Hierarchical Bayes}},
author = {Schwaighofer, Anton and Tresp, Volker and Yu, Kai},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1209-1216},
url = {https://mlanthology.org/neurips/2004/schwaighofer2004neurips-learning/}
}