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From "Feynman Liang (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-9134) LDA Asymmetric topic-word prior
Date Fri, 17 Jul 2015 08:24:04 GMT
Feynman Liang created SPARK-9134:
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             Summary: 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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