Learning graphical models for hypothesis testing and classification

Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques to exploit this modeling ability for binary classification by discriminatively learning such models from labeled training data, i.e., using both positive and negative samples to optimize for the structures of the two models. We motivate why it is difficult to adapt existing generative methods, and propose an alternative method consisting of two parts. First, we develop a novel method to learn tree-structured graphical models which optimizes an approximation of the log-likelihood ratio. We also formulate a joint objective to learn a nested sequence of optimal forests-structured models. Second, we construct a classifier by using ideas from boosting to learn a set of discriminative trees. The final classifier can interpreted as a likelihood ratio test between two models with a larger set of pairwise features. W...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher III, Alan S. Willsky
• Keywords: Artificial Intelligence, Generative Methods, Graphical Models, Sparse Graphical Models, TSP 2010