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A Simple Stochastic Gradient Variational Bayes for the Correlated Topic Model

  • Tomonari Masada
  • Atsuhiro Takasu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

This paper proposes a new inference for the correlated topic model (CTM) [3]. CTM is an extension of LDA [4] for modeling correlations among latent topics. The proposed inference is an instance of the stochastic gradient variational Bayes (SGVB) [7, 8]. By constructing the inference network with the diagonal logistic normal distribution, we achieve a simple inference. Especially, there is no need to invert the covariance matrix explicitly. We performed a comparison with LDA in terms of predictive perplexity. The two inferences for LDA are considered: the collapsed Gibbs sampling (CGS) [5] and the collapsed variational Bayes with a zero-order Taylor expansion approximation (CVB0) [1]. While CVB0 for LDA gave the best result, the proposed inference achieved the perplexities comparable with those of CGS for LDA.

Keywords

Covariance Matrix Word Token Topic Probability Movie Review Text Mining Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: UAI, pp. 27–34 (2009)Google Scholar
  2. 2.
    Bartz, D., Müller, K.R.: Generalizing analytic shrinkage for arbitrary covariance structures. In: NIPS 26, pp. 1869–1877 (2013)Google Scholar
  3. 3.
    Blei, D.M., Lafferty, J.D.: Correlated topic models. In: NIPS, pp. 147–154 (2005)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. PNAS 101(Suppl 1), 5228–5235 (2004)CrossRefGoogle Scholar
  6. 6.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)Google Scholar
  7. 7.
    Kingma, D.P., Welling, M.: Stochastic gradient VB and the variational auto-encoder. In: ICLR (2014)Google Scholar
  8. 8.
    Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: ICML, pp. 1278–1286 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Nagasaki UniversityNagasakiJapan
  2. 2.National Institute of InformaticsTokyoJapan

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