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Learning an L1-regularized Gaussian Bayesian network in the equivalence class space

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  • Diego Vidaurre
  • Concha Bielza, Polytechnic University of Madrid
  • ,
  • Pedro Larrañaga, Polytechnic University of Madrid

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant.

Original languageEnglish
Article number5382574
JournalIEEE Transactions on Cybernetics
Pages (from-to)1231-1242
Number of pages12
Publication statusPublished - 1 Oct 2010
Externally publishedYes

    Research areas

  • Equivalence class, gene networks, graphical Gaussian model, k-greedy equivalence search (GES), Lasso, microarrays, network induction, regularization

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