Latent Dirichlet Allocation

@article{Blei2003LatentDA,
  title={Latent Dirichlet Allocation},
  author={David M. Blei and Andrew Y. Ng and Michael I. Jordan},
  journal={Journal of Machine Learning Research},
  year={2003},
  volume={3},
  pages={993-1022}
}
We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable… CONTINUE READING
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