Learning spatial-temporal varying graphs with applications to climate data analysis

Xi Chen, Yan Liu, Han Liu, Jaime G. Carbonell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

An important challenge in understanding climate change is to uncover the dependency relationships between various climate observations and forcing factors. Graphical lasso, a recently proposed ℓ1 penalty based structure learning algorithm, has been proven successful for learning underlying dependency structures for the data drawn from a multivariate Gaussian distribution. However, climatological data often turn out to be non-Gaussian, e.g. cloud cover, precipitation, etc. In this paper, we examine nonparametric learning methods to address this challenge. In particular, we develop a methodology to learn dynamic graph structures from spatial-temporal data so that the graph structures at adjacent time or locations are similar. Experimental results demonstrate that our method not only recovers the underlying graph well but also captures the smooth variation properties on both synthetic data and climate data.

Original languageEnglish (US)
Title of host publicationAAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
PublisherAI Access Foundation
Pages425-430
Number of pages6
ISBN (Print)9781577354642
StatePublished - Jan 1 2010
Event24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, GA, United States
Duration: Jul 11 2010Jul 15 2010

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
CountryUnited States
CityAtlanta, GA
Period7/11/107/15/10

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Chen, X., Liu, Y., Liu, H., & Carbonell, J. G. (2010). Learning spatial-temporal varying graphs with applications to climate data analysis. In AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference (pp. 425-430). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1). AI Access Foundation.