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How to Supervise Topic Models

  • Cheng ZhangEmail author
  • Hedvig Kjellström
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

Supervised topic models are important machine learning tools which have been widely used in computer vision as well as in other domains. However, there is a gap in the understanding of the supervision impact on the model. In this paper, we present a thorough analysis on the behaviour of supervised topic models using Supervised Latent Dirichlet Allocation (SLDA) and propose two factorized supervised topic models, which factorize the topics into signal and noise. Experimental results on both synthetic data and real-world data for computer vision tasks show that supervision need to be boosted to be effective and factorized topic models are able to enhance the performance.

Keywords

Topic modeling SLDA LDA Factorized supervised topic models 

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Supplementary material

336126_1_En_39_MOESM1_ESM.zip (321 kb)
Supplementary material (ZIP 321KB)

References

  1. 1.
    Blei, D.M., Lafferty, J.: Correlated topic models. In: NIPS (2006)Google Scholar
  2. 2.
    Blei, D.M., McAuliffe, J.D.: Supervised topic models, arxiv:1003.0783 (2010)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Browne, M.W.: The maximum-likelihood solution in inter-battery factor analysis. British Journal of Mathematical and Statistical Psychology 32(1), 75–86 (2011)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Cao, L., Fei-Fei, L.: Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes. In: ICCV (2007)Google Scholar
  6. 6.
    Chang, J., Boyd-Graber, J., Wang, C., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: NIPS (2009)Google Scholar
  7. 7.
    Damianou, A., Ek, C.H., Titsias, M.K., Lawrence, N.D.: Manifold relevance determination. In: ICML, pp. 145–152 (2012)Google Scholar
  8. 8.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR (2005)Google Scholar
  9. 9.
    Hoffman, M.D., Blei, D.M., Bach, F.: Online learning for latent Dirichlet allocation. In: NIPS (2010)Google Scholar
  10. 10.
    Hofmann, T.: Probabilistic latent semantic analysis. In: ACM SIGIR (1999)Google Scholar
  11. 11.
    Hospedales, T.M., Gong, S.G., Xiang, T.: Learning tags from unsegemented videos of multiple human actions (2011)Google Scholar
  12. 12.
    Lacoste-Julien, S., Sha, F., Jordan, M.I.: DiscLDA: discriminative learning for dimensionality reduction and classification. In: NIPS (2008)Google Scholar
  13. 13.
    Laptev, I., Lindeberg, T.: Local descriptors for spatio-temporal recognition. In: MacLean, W.J. (ed.) SCVMA 2004. LNCS, vol. 3667, pp. 91–103. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)Google Scholar
  15. 15.
    Niu, Z., Hua, G., Gao, X., Tian, Q.: Semi-supervised relational topic model for weakly annotated image recognition in social media. In: CVPR (2014)Google Scholar
  16. 16.
    Rabinovich, M., Blei, D.M.: The inverse regression topic model. In: ICML (2014)Google Scholar
  17. 17.
    Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Conference on Empirical Methods in Natural Language Processing (2009)Google Scholar
  18. 18.
    Rasiwasia, N., Vasconcelos, N.: Latent Dirichlet allocation models for image classification. PAMI 35(11), 2665–2679 (2013)CrossRefGoogle Scholar
  19. 19.
    Tang, J., Meng, Z., Nguyen, X., Mei, Q., Zhang, M.: Understanding the limiting factors of topic modeling via posteriror contraction analysis. In: ICML (2014)Google Scholar
  20. 20.
    Tucker, L.R.: An Inter-Battery Method of Factory Analysis. Psychometrika 23, June 1958Google Scholar
  21. 21.
    Wang, C., Blei, D.: Collaborative topic modeling for recommending scientific articles. In: ACM SIGKDD (2011)Google Scholar
  22. 22.
    Wang, C., Blei, D.M., Fei-Fei, L.: Simultaneous image classification and annotation. In: CVPR (2009)Google Scholar
  23. 23.
    Wang, C., Paisley, J., Blei, D.: Online variational inference for the hierarchical Dirichlet process. In: AISTATS (2011)Google Scholar
  24. 24.
    Weinshall, D., Levi, G., Hanukaev, D.: Latent Dirichlet allocation topic model with soft assignment of descriptors to words. In: ICML (2013)Google Scholar
  25. 25.
    Zhang, C., Ek, C.H., Damianou, A., Kjellström, H.: Factorized topic models. In: International Conference on Learning Representations (2013)Google Scholar
  26. 26.
    Zhang, C., Ek, C.H., Gratal, X., Pokorny, F.T., Kjellström, H.: Supervised hierarchical Dirichlet processes with variational inference. In: ICCV Workshop on Inference for Probabilistic Graphical Models (2013)Google Scholar
  27. 27.
    Zhu, J., Ahmed, A., Xing, E.P.: Medlda: maximum margin supervised topic models for regression and classification. In: ICML (2009)Google Scholar
  28. 28.
    Zhu, J., Chen, N., Perkins, H., Zhang, B.: Gibbs max-margin supervised topic models with fast sampling algorithms. In: ICML (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Vision and Active Perception Lab, Centre for Autonomous SystemsKTH Royal Institute of TechnologyStockholmSweden

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