DeepAI AI Chat
Log In Sign Up

A global approach for learning sparse Ising models

by   Daniela De Canditiis, et al.
Consiglio Nazionale delle Ricerche

We consider the problem of learning the link parameters as well as the structure of a binary-valued pairwise Markov model. 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, in a global manner. The idea behind this proposal is to exploit the reciprocal information of the nodes between each other during the estimation process. Detailed numerical experiments highlight the advantage of this technique and confirm the intuition behind it.


page 1

page 2

page 3

page 4


An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation

The pseudo-likelihood method is one of the most popular algorithms for l...

Language-Constraint Reachability Learning in Probabilistic Graphs

The probabilistic graphs framework models the uncertainty inherent in re...

Structure Learning for Relational Logistic Regression: An Ensemble Approach

We consider the problem of learning Relational Logistic Regression (RLR)...

Nonconvex Sparse Logistic Regression with Weakly Convex Regularization

In this work we propose to fit a sparse logistic regression model by a w...

Hyperparameter optimization with approximate gradient

Most models in machine learning contain at least one hyperparameter to c...

Common and Individual Structure of Multiple Networks

This article focuses on the problem of studying shared- and individual-s...

Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy

There has been an ever-increasing interest in multidisciplinary research...