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https://issues.apache.org/jira/browse/SPARK-5564?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14387180#comment-14387180
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Debasish Das edited comment on SPARK-5564 at 3/30/15 6:52 PM:
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Cool...I will run my experiments on the same dataset as well and report
results...By the way my plan is to run 1000 sparse topics here...K will be 1000
but sparse and so we never shuffle more than 100 sparse vectors...For sparsity
experiments did you also add something specific ?
was (Author: debasish83):
Cool...I will run my experiments on the same dataset as well and report
results...
> 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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