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https://issues.apache.org/jira/browse/SPARK-5564?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14343476#comment-14343476
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Joseph K. Bradley commented on SPARK-5564:
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It would be interesting to see comparisons between the two, but I don't have a
good sense of which would be more efficient.
{quote} I am assuming here that LDA architecture is a bipartite graph with
nodes as docs/words and there are counts on each edge {quote}
--> You're correct.
> Support sparse LDA solutions
> ----------------------------
>
> Key: SPARK-5564
> URL: https://issues.apache.org/jira/browse/SPARK-5564
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
>
> Latent Dirichlet Allocation (LDA) currently requires that the priors’
> concentration parameters be > 1.0. It should support values > 0.0, which
> should encourage sparser topics (phi) and document-topic distributions
> (theta).
> For EM, this will require adding a projection to the M-step, as in: Vorontsov
> and Potapenko. "Tutorial on Probabilistic Topic Modeling : Additive
> Regularization for Stochastic Matrix Factorization." 2014.
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