A global approach for learning sparse Ising models

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 $l_1$- 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.

arXiv comment15 pages, 4 figures. arXiv admin note: text overlap with arXiv:1902.04728 by other authors

References in corpus (4)

• High-dimensional Ising model selection using ${\ell }_1$-regularized logistic regression
• High-dimensional structure estimation in Ising models: Local separation criterion
• Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
• Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

Cited by in corpus (1)

• Learning binary undirected graph in low dimensional regime