Partially Observed Maximum Entropy Discrimination Markov Networks

Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unattainable fully annotated training data. While likelihood-based methods have been extensively explored, to our knowledge, learning structured prediction models with latent variables based on the max-margin principle remains largely an open problem. In this paper, we present a partially observed Maximum Entropy Discrimination Markov Network (PoMEN) model that attempts to combine the advantages of Bayesian and margin based paradigms for learning Markov networks from partially labeled data. PoMEN leads to an averaging prediction rule that resembles a Bayes predictor that is more robust to overfitting, but is also built on the desirable discriminative laws resemble those of the M3 N. We develop an EM-style algorithm utilizing existing convex optimization algorithms for M3 N as a subroutine. We demonstrate competent ...

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Jun Zhu, Eric P. Xing, Bo Zhang
• Keywords: Information Technology, Markov Networks, NIPS 2008, Salient Structured Predictors, Structured Prediction Models