PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data
Jaakko Luttinen and Alexander Ilin
In: NIPS 2009, 6-9 Dec 2009, Vancouver, Canada.

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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6408
Deposited By:Alexander Ilin
Deposited On:08 March 2010