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From "Mohamed Baddar (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-9134) LDA Asymmetric topic-word prior
Date Tue, 15 Mar 2016 12:36:33 GMT

    [ https://issues.apache.org/jira/browse/SPARK-9134?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15195217#comment-15195217
] 

Mohamed Baddar commented on SPARK-9134:
---------------------------------------

[~josephkb] [~fliang] If no body working on that , and there is an interest in that issue
, can i start working on it ?

> LDA Asymmetric topic-word prior
> -------------------------------
>
>                 Key: SPARK-9134
>                 URL: https://issues.apache.org/jira/browse/SPARK-9134
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Feynman Liang
>
> SPARK-8536 generalizes LDA to asymmetric document-topic priors, which [Wallach et al|http://dirichlet.net/pdf/wallach09rethinking.pdf]
proposes offers greater utility in terms of asymmetric priors.
> However, [Stanford NLP|http://nlp.stanford.edu/software/tmt/tmt-0.2/scaladocs/scaladocs/edu/stanford/nlp/tmt/lda/LDA.html]
also permits asymmetric priors on the topic-word prior. We should not support manually specifying
the entire matrix (which has numTopics * vocabSize entries); rather we should follow Stanford
NLP and take a single vector of length vocabSize for a prior over words and assume that all
topics share this prior (e.g. replicate it numTopics times to get the topic-word prior matrix).
> We are leaving this as todo; any users who have a need for this feature should discuss
on this JIRA.



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