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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/08/03 21:56:05 UTC
[jira] [Updated] (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 updated SPARK-5564:
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Target Version/s: 1.6.0 (was: 1.5.0)
> 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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