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https://issues.apache.org/jira/browse/SPARK-5563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14305115#comment-14305115
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yuhao yang edited comment on SPARK-5563 at 2/4/15 2:22 PM:
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Thanks Joseph for helping create the jira.
Paste previous [comment
link|https://issues.apache.org/jira/browse/SPARK-1405?focusedCommentId=14302952&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14302952]
here and share the current implementation at
https://github.com/hhbyyh/OnlineLDA_Spark.
I agree with the suggestion listed above and will propose a PR for more
detailed discussion soon. Thanks.
was (Author: yuhaoyan):
Thanks Joseph for helping create the jira.
Paste previous [comment
link|https://issues.apache.org/jira/browse/SPARK-1405?focusedCommentId=14302952&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14302952]
here and share the current implementation at
https://github.com/hhbyyh/OnlineLDA_Spark.
I agree with the suggestion listed above and will propose a PR for more
detailed discussion soon. Thanks
> LDA with online variational inference
> -------------------------------------
>
> Key: SPARK-5563
> URL: https://issues.apache.org/jira/browse/SPARK-5563
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
>
> Latent Dirichlet Allocation (LDA) parameters can be inferred using online
> variational inference, as in Hoffman, Blei and Bach. “Online Learning for
> Latent Dirichlet Allocation.” NIPS, 2010. This algorithm should be very
> efficient and should be able to handle much larger datasets than batch
> algorithms for LDA.
> This algorithm will also be important for supporting Streaming versions of
> LDA.
> The implementation will ideally use the same API as the existing LDA but use
> a different underlying optimizer.
> This will require hooking in to the existing mllib.optimization frameworks.
> This will require some discussion about whether batch versions of online
> variational inference should be supported, as well as what variational
> approximation should be used now or in the future.
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