Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Vidaurre Henche, Diego and Bielza, Concha and Larrañaga Múgica, Pedro (2010). Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space. "IEEE Transactions on Systems, Man and Cybernetics, Part B", v. 40 (n. 5); pp. 1231-1242. ISSN 1083-4419.

Description

Title: Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space
Author/s:
  • Vidaurre Henche, Diego
  • Bielza, Concha
  • Larrañaga Múgica, Pedro
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Systems, Man and Cybernetics, Part B
Date: October 2010
ISSN: 1083-4419
Volume: 40
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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

More information

Item ID: 10999
DC Identifier: http://oa.upm.es/10999/
OAI Identifier: oai:oa.upm.es:10999
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5382574
Deposited by: Memoria Investigacion
Deposited on: 05 Jun 2012 08:46
Last Modified: 20 Apr 2016 19:10
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