Learning graphical models for hypothesis testing and classification
Name
Willsky_Learning graphical.pdf
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
53896d0aeda396cd6097f3eb8aba18fd
Author(s)
Tan, Vincent Yan Fu
Sanghavi, Sujay
Fisher, John W., III
Willsky, Alan S.
Date Issued
July 2010
Journal
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Tan, Vincent Y. F. et al. “Learning Graphical Models for Hypothesis Testing and Classification.” IEEE Transactions on Signal Processing 58.11 (2010): 5481–5495. © Copyright 2010 IEEE
Version
Final published version
Abstract
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. We use cross-validation to determine the optimal number of edges in the final model. The algorithm presented in this paper also provides a method to identify a subset of the edges that are most salient for discrimination. Experiments show that the proposed procedure outperforms generative methods such as Tree Augmented Naïve Bayes and Chow-Liu as well as their boosted counterparts.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Stochastic Systems Group
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TSP.2010.2059019
