Inferring large graphs using l1-penalized likelihood

Abstract : We address the issue of recovering the structure of large sparse directed acyclic graphs from noisy observations of the system. We propose a novel procedure based on a specific formulation of the l1-norm regularized maximum likelihood, which decomposes the graph estimation into two optimization sub-problems: topological structure and node order learning. We provide oracle inequalities for the graph estimator, as well as an algorithm to solve the induced optimization problem, in the form of a convex program embedded in a genetic algorithm. We apply our method to various data sets (including data from the DREAM4 challenge) and show that it compares favorably to state-of-the-art methods.
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Article dans une revue
Statistics and Computing, Springer Verlag (Germany), 2017, 〈10.1198/jasa.2011.ap10346〉
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Soumis le : mercredi 4 octobre 2017 - 12:00:09
Dernière modification le : jeudi 11 janvier 2018 - 06:19:45


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Magali Champion, Victor Picheny, Matthieu Vignes. Inferring large graphs using l1-penalized likelihood. Statistics and Computing, Springer Verlag (Germany), 2017, 〈10.1198/jasa.2011.ap10346〉. 〈hal-01172745v3〉



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