Hierarchical Gaussian Process Latent Variable Models
Abstract
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.
Cite
Text
Lawrence and Moore. "Hierarchical Gaussian Process Latent Variable Models." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273557Markdown
[Lawrence and Moore. "Hierarchical Gaussian Process Latent Variable Models." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/lawrence2007icml-hierarchical/) doi:10.1145/1273496.1273557BibTeX
@inproceedings{lawrence2007icml-hierarchical,
title = {{Hierarchical Gaussian Process Latent Variable Models}},
author = {Lawrence, Neil D. and Moore, Andrew J.},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {481-488},
doi = {10.1145/1273496.1273557},
url = {https://mlanthology.org/icml/2007/lawrence2007icml-hierarchical/}
}