Class meeting for 10-605 LDA

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This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.




Basic LDA:

  • Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.

Speedups for LDA:

Things to remember

  • How Gibbs sampling is used to sample from a model.
  • The "generative story" associated with key models like LDA, naive Bayes, and stochastic block models.
  • What a "mixed membership" generative model is.
  • The time complexity and storage requirements of Gibbs sampling for LDAs.
  • How LDA learning can be sped up using IPM approaches.
  • Why efficient sampling is important for LDAs
  • How sampling can be sped up for many topics by preprocessing the parameters of the distribution
  • How the storage used for LDA can be reduced by exploiting the fact that many words are rare.