Learning DAGs without imposing acyclicity

Abstract

We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we frame structural learning as a sparse matrix factorization problem and we empirically show that solving an ${\ell }_1$-penalized optimization yields to good recovery of the true graph and, in general, to almost-DAG graphs. Moreover, this approach is computationally efficient and is not affected by the explosion of combinatorial complexity as in classical structural learning algorithms.

16 pages, 5 figures

References in corpus (3)

• A Transformational Characterization of Equivalent Bayesian Network Structures
• Directed Cyclic Graphical Representations of Feedback Models
• Graphical continuous Lyapunov models