Pathway Graphical Lasso
Graphical models provide a rich framework for summarizing the dependencies among variables. The approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an penalty on the elements of the inverse co-variance matrix. Most alg...
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| Published in: | Proceedings of the ... AAAI Conference on Artificial Intelligence Vol. 2015; pp. 2617 - 2623 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
01.01.2015
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| ISSN: | 2159-5399 |
| Online Access: | Get more information |
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| Summary: | Graphical models provide a rich framework for summarizing the dependencies among variables. The
approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an
penalty on the elements of the inverse co-variance matrix. Most algorithms for solving the graphical lasso problem do not scale to a very large number of variables. Furthermore, the learned network structure is hard to interpret. To overcome these challenges, we propose a novel GGM structure learning method that exploits the fact that for many real-world problems we have prior knowledge that certain edges are unlikely to be present. For example, in gene regulatory networks, a pair of genes that does not participate together in any of the cellular processes, typically referred to as
, is less likely to be connected. In computer vision applications in which each variable corresponds to a pixel, each variable is likely to be connected to the nearby variables. In this paper, we propose the
, which learns the structure of a GGM subject to pathway-based constraints. In order to solve this problem, we decompose the network into smaller parts, and use a message-passing algorithm in order to communicate among the subnetworks. Our algorithm has orders of magnitude improvement in run time compared to the state-of-the-art optimization methods for the graphical lasso problem that were modified to handle pathway-based constraints. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2159-5399 |
| DOI: | 10.1609/aaai.v29i1.9636 |