Gaussian Process Dynamical Models
Abstract
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian Process (GP) priors for both the dynamics and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach on human motion capture data in which each pose is 62-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces. Webpage: http://www.dgp.toronto.edu/
Cite
Text
Wang et al. "Gaussian Process Dynamical Models." Neural Information Processing Systems, 2005.Markdown
[Wang et al. "Gaussian Process Dynamical Models." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/wang2005neurips-gaussian/)BibTeX
@inproceedings{wang2005neurips-gaussian,
title = {{Gaussian Process Dynamical Models}},
author = {Wang, Jack and Hertzmann, Aaron and Fleet, David J},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {1441-1448},
url = {https://mlanthology.org/neurips/2005/wang2005neurips-gaussian/}
}