MedLDA

Jun Zhu, Amr Ahmed, Eric P. Xing
2009 Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09  
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihoodbased estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic
more » ... ve topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.
doi:10.1145/1553374.1553535 dblp:conf/icml/ZhuAX09 fatcat:5xmrakcb4rehvlgywmx5jewqvu