[jira] [Issue Comment Deleted] (SPARK-9134) LDA Asymmetric topic-word prior

["Mohamed Baddar (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-9134?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mohamed Baddar updated SPARK-9134:
----------------------------------
    Comment: was deleted

(was: [~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.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org