A Sparsification Approach for Temporal Graphical Model Decomposition

Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct temporal causal graphical models. However, how to best understand and conceptualize these complicated causal relationships is still an open problem. In this paper, we propose a decomposition approach to simplify the temporal graphical model. Our method clusters time series variables into groups such that strong interactions appear among the variables within each group and weak (or no) interactions exist for crossgroup variable pairs. Specifically, we formulate the clustering problem for temporal graphical models as a regressioncoefficient sparsification problem and define an interesting objective function which balances the model prediction power and its cluster structure. We introduce an iterative optimization approach utilizing the Quasi-Newton method and generalized ridge regression to minimize the objective...

• Type: Journal
• Year: 2009
• Where: ICDM
• Authors: Ning Ruan, Ruoming Jin, Victor E. Lee, Kun Huang
• Keywords: Data Mining, Graphical Model, ICDM 2009, Temporal Causal, Temporal Graphical Model