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Article
Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis
Institute for Software Research
  • Xi Chen, Carnegie Mellon University
  • Yan Liu
  • Han Liu, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
Date of Original Version
1-1-2010
Type
Conference Proceeding
Abstract or Description
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.
Citation Information
Xi Chen, Yan Liu, Han Liu and Jaime G. Carbonell. "Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis" (2010)
Available at: http://works.bepress.com/jaime_carbonell/130/