Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data
Abstract
We present a probabilistic latent factor model which can be used for studying spatio-temporal datasets. The spatial and temporal structure is modeled by using Gaussian process priors both for the loading matrix and the factors. The posterior distributions are approximated using the variational Bayesian framework. High computational cost of Gaussian process modeling is reduced by using sparse approximations. The model is used to compute the reconstructions of the global sea surface temperatures from a historical dataset. The results suggest that the proposed model can outperform the state-of-the-art reconstruction systems.
Cite
Text
Luttinen and Ilin. "Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data." Neural Information Processing Systems, 2009.Markdown
[Luttinen and Ilin. "Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data." Neural Information Processing Systems, 2009.](https://mlanthology.org/neurips/2009/luttinen2009neurips-variational/)BibTeX
@inproceedings{luttinen2009neurips-variational,
title = {{Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data}},
author = {Luttinen, Jaakko and Ilin, Alexander},
booktitle = {Neural Information Processing Systems},
year = {2009},
pages = {1177-1185},
url = {https://mlanthology.org/neurips/2009/luttinen2009neurips-variational/}
}