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 1-norm regularized maximumlikelihood, which decomposes the graph estimation into two optimization sub-problems: topological structure and node order learning. We provide convergence 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. This algorithm is available onCRANas theRpackage GADAG.
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Statistics and Computing, Springer Verlag (Germany), 2017, 〈10.1007/s11222-017-9769-z〉
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Soumis le : lundi 2 octobre 2017 - 18:59:52
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Magali Champion, Victor Picheny, Matthieu Vignes. Inferring large graphs using l1 -penalized likelihood. Statistics and Computing, Springer Verlag (Germany), 2017, 〈10.1007/s11222-017-9769-z〉. 〈hal-01602560〉

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