Spanning Tree Approximations for Conditional Random Fields
Abstract
In this work we show that one can train Conditional Random Fields of intractable graphs effectively and efficiently by considering a mixture of random spanning trees of the underlying graphical model. Furthermore, we show how a maximum-likelihood estimator of such a training objective can subsequently be used for prediction on the full graph. We present experimental results which improve on the state-of-the-art. Additionally, the training objective is less sensitive to the regularization than pseudo-likelihood based training approaches. We perform the experimental validation on two classes of data sets where structure is important: image denoising and multilabel classification.
Cite
Text
Pletscher et al. "Spanning Tree Approximations for Conditional Random Fields." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.Markdown
[Pletscher et al. "Spanning Tree Approximations for Conditional Random Fields." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.](https://mlanthology.org/aistats/2009/pletscher2009aistats-spanning/)BibTeX
@inproceedings{pletscher2009aistats-spanning,
title = {{Spanning Tree Approximations for Conditional Random Fields}},
author = {Pletscher, Patrick and Ong, Cheng Soon and Buhmann, Joachim},
booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
year = {2009},
pages = {408-415},
volume = {5},
url = {https://mlanthology.org/aistats/2009/pletscher2009aistats-spanning/}
}