Bounding Sample Errors in Approximate Distributed Latent Dirichlet Allocation

@inproceedings{Ihler2009BoundingSE,
  title={Bounding Sample Errors in Approximate Distributed Latent Dirichlet Allocation},
  author={Alexander T. Ihler and David Newman},
  year={2009}
}
Latent Dirichlet allocation (LDA) is a popular algorithm for discovering structure in large collections of text or other data. Although its complexity is linear in the data size, its use on increasingly massive collections has created considerable interest in parallel implementations. “Approximate distributed” LDA, or AD-LDA, approximates the popular… CONTINUE READING