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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/07/20 18:45:05 UTC

[jira] [Closed] (SPARK-5564) Support sparse LDA solutions

     [ https://issues.apache.org/jira/browse/SPARK-5564?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley closed SPARK-5564.
------------------------------------
    Resolution: Later

Closing to reset progress status to Open.

> 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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