Learning Graphical Model Structure using L1-Regularization Paths

Mark W. Schmidt, Alexandru Niculescu-Mizil, Kevin P. Murphy

Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability of the MDL score, plus the ability to quickly compute entire regularization paths, allows us to efficiently pick the optimal regularization parameter on a per-node basis. Second, we show how to use L1 variable selection to select the Markov blanket, before a DAG search stage. Finally, we show how L1 variable selection can be used inside of an order search algorithm. The effectiveness of these L1-based approaches are compared to current state of the art methods on 10 datasets.

Subjects: 12. Machine Learning and Discovery; Please choose a second document classification

Submitted: Apr 24, 2007

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.