Latent Gaussian Process Regression
Machine Learning
2017-09-19 v2 Machine Learning
Abstract
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can be used to model multi-modal and non-stationary processes. We exemplify the approach on a set of synthetic data and provide results on real data from motion capture and geostatistics.
Cite
@article{arxiv.1707.05534,
title = {Latent Gaussian Process Regression},
author = {Erik Bodin and Neill D. F. Campbell and Carl Henrik Ek},
journal= {arXiv preprint arXiv:1707.05534},
year = {2017}
}