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https://issues.apache.org/jira/browse/SPARK-5564?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14342312#comment-14342312
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Debasish Das commented on SPARK-5564:
-------------------------------------
By the way the following step is an approximation to the real constraint but if
we get good results over Gibbs Sampling based approaches, there are ways to
solve the real problem as well...
minimize f(w*,h)
s.t 0 <= h <= 1, Normalize each column in h
> 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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