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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 24 / No. 1: Twenty-Fourth AAAI Conference on Artificial Intelligence

Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis

March 8, 2023

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Authors

Xi Chen

Carnegie Mellon University


Yan Liu

IBM T. J. Watson Research Center


Han Liu

Carnegie Mellon University


Jaime Carbonell

Carnegie Mellon University


DOI:

10.1609/aaai.v24i1.7658


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. 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 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.

Topics: AAAI

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HOW TO CITE:

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis Proceedings of the AAAI Conference on Artificial Intelligence, 24 (2010) 425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis AAAI 2010, 425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell (2010). Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 24, 425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell. Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 24 2010 p.425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell. 2010. Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. "Proceedings of the AAAI Conference on Artificial Intelligence, 24". 425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell. (2010) "Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis", Proceedings of the AAAI Conference on Artificial Intelligence, 24, p.425

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell, "Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis", AAAI, p.425, 2010.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell. "Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis". Proceedings of the AAAI Conference on Artificial Intelligence, 24, 2010, p.425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell. "Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis". Proceedings of the AAAI Conference on Artificial Intelligence, 24, (2010): 425.

Xi Chen|| Yan Liu|| Han Liu|| Jaime Carbonell. Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. AAAI[Internet]. 2010[cited 2023]; 425.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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