Latent Dirichlet Allocation Modeling Algorithm

A Latent Dirichlet Allocation Modeling Algorithm is a generative model training algorithm that can be applied by a latent Dirichlet modeling system to produce a Latent Dirichlet Allocation model.

• AKA: LDA Parameter Estimation Algorithm.
• Context:
  • It requires Parameter Approximation), e.g. based on:
    • variational inference with EM algorithm (Blei, Ng & Jordan, 2003).
    • Gibbs sampling (Steyvers & Griffiths, 2005).
  • …
• Counter-Example(s):
  • Latent Semantic Analysis Algorithm.
  • Hierarchical Dirichlet Process Algorithm.
  • Random Projections Algorithm.
• See: Probabilistic Latent Semantic Analysis Algorithm.

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References

2005

• (Steyvers & Griffiths, 2005) ⇒ Mark Steyvers, and Thomas L. Griffiths. (2005). “Probabilistic Topic Models.” In: Thomas K. Landauer (editor), D. Mcnamara (editor), S. Dennis (editor), and W. Kintsch (editor). “Latent Semantic Analysis: A Road to Meaning.” Laurence Erlbaum.

2003

• (Blei, Ng & Jordan, 2003) ⇒ David M. Blei, Andrew Y. Ng , and Michael I. Jordan. (2003). “Latent Dirichlet Allocation.” In: The Journal of Machine Learning Research, 3.

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