Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data

Reference:

Jaakko Luttinen and Alexander Ilin. Variational Gaussian-process factor analysis for modeling spatio-temporal data. In Advances in Neural Information Processing Systems 22, pages 1177–1185. MIT Press, Cambridge, MA, USA, 2009.

Abstract:

We present a probabilistic factor analysis 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.

Suggested BibTeX entry:

@incollection{Luttinen09nips,
    address = {Cambridge, MA, USA},
    author = {Jaakko Luttinen and Alexander Ilin},
    booktitle = {Advances in Neural Information Processing Systems 22},
    pages = {1177--1185},
    publisher = {MIT Press},
    title = {Variational {Gaussian}-Process Factor Analysis for Modeling Spatio-Temporal Data},
    year = {2009},
}