A global approach for learning sparse Ising models

Daniela De Canditiis

Mathematics and Computers in Simulation (MATCOM), 2020, vol. 176, issue C, 160-170

Abstract: We consider the problem of learning the link parameters as well as the structure of a binary-valued pairwise Markov model. Under sparsity assumption, we propose a method based on l1-regularized logistic regression, which estimate globally the whole set of edges and link parameters. Unlike the more recent methods discussed in literature that learn the edges and the corresponding link parameters one node at a time, in this work we propose a method that learns all the edges and corresponding link parameters simultaneously for all nodes. The idea behind this proposal is to exploit the reciprocal information of the nodes between each other during the estimation process. Numerical experiments highlight the advantage of this technique and confirm the intuition behind it.

Keywords: Ising models; Pairwise Markov Graphs; l1 penalty; Logistic regression (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:176:y:2020:i:c:p:160-170

DOI: 10.1016/j.matcom.2020.02.012

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