Learning Graphical Models for Hypothesis Testing and Classification

  title={Learning Graphical Models for Hypothesis Testing and Classification},
  author={Vincent Y. F. Tan and Sujay Sanghavi and John W. Fisher and Alan S. Willsky},
  journal={IEEE Transactions on Signal Processing},
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… CONTINUE READING
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