Papers on LDA

最近要做LDA和Topic Model的seminar,顺便把整理出来的一些文献放在这里。
其实D.Blei主页上面已经有一个列表了,是David Mimno维护的,但是那个列表对于我等初入此门的菜鸟来说还有很多前续的文献要读。(此列表要用IE内核浏览器打开)
下面是我的列表

On Latent Dirichlet Allocation:

  1. David M Blei, Andrew Y Ng, and Michael I Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993–1022, 2003
  2. David M Blei and John D Lafferty. Topic models. Taylor and Francis, 2009.
  3. Ali Daud, Juanzi Li, Lizhu Zhou, and Faqir Muhammad. Knowledge discovery through directed probabilistic topic models: a survey. Frontiers of Computer Science in China, 4(2):280–301,January 2010.
  4. Mark Steyvers and Tom Griffith. Probabilistic topic models. Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum, July 2006.

On variational inference:

  1. Martin Wainwright. Graphical models and variational methods:Message-passing, convex relaxations, and all that. ICML2008 Tutorial
  2. M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, Vol. 1, Numbers 1–2, pp. 1–305, December 2008

On Gibbs Sampling and MCMC:

  1. D.J.C. MacKay. Information theory, inference, and learning algorithms. Cambridge Univ Pr,2003.
  2. Gregor Heinrich. Parameter estimation for text analysis. Technical Report, 2009.
  3. Michael I. Jordan and Yair Weiss. Graphical models: Probabilistic inference.
  4. Christophe Andrieu, N De Freitas, A Doucet, and Michael I. Jordan. An introduction to MCMC for machine learning. Machine learning, pages 5–43, 2003.
  5. Yi Wang. Distributed Gibbs Sampling of Latent Dirichlet Allocation : The Gritty Details. Technical Report, 2007.

On improvment of LDA Topic Model:

  1. David M. Blei and John D Lafferty.  Correlated Topic Models.  In Advances in Neural Information Processing Systems 18, 2006.
  2. David M. Blei and John D. Lafferty. Dynamic topic models. Proceedings of the 23rd international conference on Machine learning – ICML ’06, pages 113–120, 2006.
  3. Xuerui Wang and A. McCallum.  Topics over time: a non-Markov continuous time model of topical trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 424–433. ACM, 2006.

On discussion of Topic Model itself:

  1. Hanna Wallach, David Mimno, and Andrew McCallum. Rethinking LDA: Why Priors Matter. In Y Bengio, D Schuurmans, J Lafferty, C K I Williams, and A Culotta, editors, Advances in Neural Information Processing Systems 22, pages 1973–1981. 2009.
  2. Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, and David Mimno. Evaluation methods for topic models.  In Proceedings of the 26th Annual International Conference on Machine Learning – ICML ’09, pages 1–8, New York, New York, USA, 2009. ACM Press.

这个列表非常的不全,其中vision领域我没有涉猎过,所以就没有列上,有空再慢慢补上。

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