Gibbs Max-margin Topic Models with Data Augmentation

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Provided by: Journal of Machine Learning Research (JMLR)
Topic: Big Data
Format: PDF
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent paper on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. However, the resulting learning problems are usually hard to solve because of the non-smoothness of the margin loss. Existing approaches to building max-margin supervised topic models rely on an iterative procedure to solve multiple latent SVM sub-problems with additional mean-field assumptions on the desired posterior distributions.
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