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MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification

MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification. J. Zhu, A. Ahmed and E.P. Xing Carnegie Mellon University ICML 2009. Presented By Haojun Chen. Sources: http://www.cs.cmu.edu/~junzhu/medlda.htm. Outline. Motivation

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MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification

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  1. MedLDA: Maximum Margin Supervised Topic Models forRegression and Classification J. Zhu, A. Ahmed and E.P. Xing Carnegie Mellon University ICML 2009 Presented By Haojun Chen Sources: http://www.cs.cmu.edu/~junzhu/medlda.htm

  2. Outline • Motivation • Supervised topic model (sLDA) and Support vector regression (SVR) • Maximum entropy discrimination LDA (MedLDA) • MedLDA for Regression • MedLDA for Classification • Experiments Results • Conclusion

  3. Motivation • Learning latent topic models with side information, like sLDA, has attracted increasingly attention. • Maximum likelihood estimation are used for posterior inference and parameter estimation in sLDA. • Max-margin methods, such as SVM, for classification have demonstrated success in many applications. • General principle for learning max-margin discriminative supervised latent topic models for both regression and classification is proposed in this paper.

  4. Supervised Topic Model (sLDA) • Joint distribution for sLDA • Variational MLE for sLDA

  5. Support Vector Regression (SVR) • Given a training set , the linear SVR finds an optimal linear function by solving the following constrained convex optimization problem

  6. Max-Entropy Discrimination LDA (MedLDA) • Maximum entropy discrimination LDA (MedLDA): an integration of max-margin prediction models (e.g. SVR and SVM) and hierarchical Bayesian topic models (e.g. LDA and sLDA) • Specifically, a distribution is learned in a max-margin manner in MedLDA. • MedLDA for regression and classification are considered in this paper.

  7. MedLDA for Regression • For regression, MedLDA is defined as an integration of Bayesian sLDA and SVR is the variational approximation for the posterior

  8. EM Algorithm for MedLDA Regression • Variational EM Algorithm: • The key difference between sLDA and MedLDA lies in updating

  9. MedLDA for Classification • Similar to the regression model, the integrated LDA and multi-class classification model is defined as follow: where

  10. EM Algorithm for MedLDA Classification • Similar to the EM algorithm for MedLDA regression • Update equation for

  11. Embedding Results • 20 Newsgroup dataset MedLDA LDA

  12. Example Topics Discovered

  13. Classification Results • 20 Newsgroup Data Relative ratio =

  14. Regression Results • Movei Review Data

  15. Time Efficiency

  16. Conclusion • MedLDA: an integration of max-margin prediction models and hierarchical Bayesian topic models by optimizing a single objective function with a set of expected margin constraints

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