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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Pré-publication, Document de travail
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Contributeur : Magali Champion <>
Soumis le : mardi 24 janvier 2017 - 17:27:42
Dernière modification le : jeudi 31 mai 2018 - 09:12:02
Document(s) archivé(s) le : mardi 25 avril 2017 - 18:22:58


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  • HAL Id : hal-01172745, version 2
  • ARXIV : 1507.02018


Magali Champion, Victor Picheny, Matthieu Vignes. Inferring large graphs using l1-penalized likelihood. 2017. 〈hal-01172745v2〉



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