Corpus ID: 88511958

MedLDA: A General Framework of Maximum Margin Supervised Topic Models

@article{Zhu2009MedLDAAG,
  title={MedLDA: A General Framework of Maximum Margin Supervised Topic Models},
  author={J. Zhu and Amr Ahmed and E. Xing},
  journal={arXiv: Machine Learning},
  year={2009}
}
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models 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… Expand
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