Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
ECML '07 Proceedings of the 18th European conference on Machine Learning
Spatial-temporal causal modeling for climate change attribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning dynamic temporal graphs for oil-production equipment monitoring system
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Sparsification Approach for Temporal Graphical Model Decomposition
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
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Causal graphical models are developed to detect the dependence relationships between random variables and provide intuitive explanations for the relationships in complex systems. Most of existing work focuses on learning a single graphical model for all the variables. However, a single graphical model cannot accurately characterize the complicated causal relationships for a relatively large graph. In this paper, we propose the problem of estimating an overlapping decomposition for Gaussian graphical models of a large scale to generate overlapping sub-graphical models. Specifically, we formulate an objective function for the overlapping decomposition problem and propose an approximate algorithm for it. A key theory of the algorithm is that the problem of solving a κ+1 node graphical model can be reduced to the problem of solving a one-step regularization based on a solved κ node graphical model. Based on this theory, a greedy expansion algorithm is proposed to generate the overlapping subgraphs. We evaluate the effectiveness of our model on both synthetic datasets and real traffic dataset, and the experimental results show the superiority of our method.