Overlapping decomposition for causal graphical modeling
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 mod...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/98826 http://hdl.handle.net/10220/12631 |
| _version_ | 1826122917344182272 |
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| author | Han, Lei Song, Guojie Cong, Gao Xie, Kunqing |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Han, Lei Song, Guojie Cong, Gao Xie, Kunqing |
| author_sort | Han, Lei |
| collection | NTU |
| description | 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 k + 1 node graphical model can be reduced to the problem of solving a one-step regularization based on a solved k 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. |
| first_indexed | 2024-10-01T05:56:06Z |
| format | Conference Paper |
| id | ntu-10356/98826 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:56:06Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/988262020-05-28T07:18:26Z Overlapping decomposition for causal graphical modeling Han, Lei Song, Guojie Cong, Gao Xie, Kunqing School of Computer Engineering International conference on Knowledge discovery and data mining (18th : 2012 : Beijing, China) DRNTU::Engineering::Computer science and engineering 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 k + 1 node graphical model can be reduced to the problem of solving a one-step regularization based on a solved k 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. 2013-07-31T06:48:21Z 2019-12-06T20:00:02Z 2013-07-31T06:48:21Z 2019-12-06T20:00:02Z 2012 2012 Conference Paper Han, L., Song, G., Cong, G., & Xie, K. (2012). Overlapping decomposition for causal graphical modeling. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12, 114-122. https://hdl.handle.net/10356/98826 http://hdl.handle.net/10220/12631 10.1145/2339530.2339551 en |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Han, Lei Song, Guojie Cong, Gao Xie, Kunqing Overlapping decomposition for causal graphical modeling |
| title | Overlapping decomposition for causal graphical modeling |
| title_full | Overlapping decomposition for causal graphical modeling |
| title_fullStr | Overlapping decomposition for causal graphical modeling |
| title_full_unstemmed | Overlapping decomposition for causal graphical modeling |
| title_short | Overlapping decomposition for causal graphical modeling |
| title_sort | overlapping decomposition for causal graphical modeling |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/98826 http://hdl.handle.net/10220/12631 |
| work_keys_str_mv | AT hanlei overlappingdecompositionforcausalgraphicalmodeling AT songguojie overlappingdecompositionforcausalgraphicalmodeling AT conggao overlappingdecompositionforcausalgraphicalmodeling AT xiekunqing overlappingdecompositionforcausalgraphicalmodeling |