Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

Bibtex Metadata Paper


Chi-hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner


We present a novel, semi-supervised approach to training discriminative random fields (DRFs) that efficiently exploits labeled and unlabeled training data to achieve improved accuracy in a variety of image processing tasks. We formulate DRF training as a form of MAP estimation that combines conditional loglikelihood on labeled data, given a data-dependent prior, with a conditional entropy regularizer defined on unlabeled data. Although the training objective is no longer concave, we develop an efficient local optimization procedure that produces classifiers that are more accurate than ones based on standard supervised DRF training. We then apply our semi-supervised approach to train DRFs to segment both synthetic and real data sets, and demonstrate significant improvements over supervised DRFs in each case.