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https://issues.apache.org/jira/browse/SPARK-5564?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14386049#comment-14386049
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Debasish Das edited comment on SPARK-5564 at 3/30/15 12:30 AM:
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[~josephkb] could you please point me to the datasets that are used for
benchmarking? I have started testing loglikelihood loss for recommendation and
since I already added the constraints, this is the right time to test it on LDA
benchmarks as well...I will open up the code as part of
https://issues.apache.org/jira/browse/SPARK-6323 as soon as our legal clears
it...
I am looking into LDA test-cases but since I am optimizing log-likelihood
directly, I am looking to add more testcases based on document and word
matrix...For recommendation, I know how to construct the testcases with
loglikelihood loss....
was (Author: debasish83):
[~josephkb] could you please point me to the datasets that are used for
benchmarking? I have started testing loglikelihood loss for recommendation and
since I already added the constraints, this is the right time to test it on LDA
benchmarks as well...I will open up the code as part of
https://issues.apache.org/jira/browse/SPARK-6323 as soon as our legal clears
it...
I am looking into LDA test-cases but since I am optimizing log-likelihood
directly, I am looking to add more testcases from your LDA JIRA...For
recommendation, I know how to construct the testcases...
> 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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