Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
Abstract
We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which additionally makes use of a standardised representation of the Gaussian process. We consider this technique for learning Mahalanobis distance metrics in a Gaussian process regression setting and provide experimental evaluations and comparisons with existing methods by considering datasets with high-dimensional inputs.
Cite
Text
Aueb and Lazaro-Gredilla. "Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression." Neural Information Processing Systems, 2013.Markdown
[Aueb and Lazaro-Gredilla. "Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/aueb2013neurips-variational/)BibTeX
@inproceedings{aueb2013neurips-variational,
title = {{Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression}},
author = {Aueb, Michalis Titsias RC and Lazaro-Gredilla, Miguel},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {279-287},
url = {https://mlanthology.org/neurips/2013/aueb2013neurips-variational/}
}