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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14303918#comment-14303918
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Joseph K. Bradley edited comment on SPARK-1405 at 2/3/15 8:29 PM:
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Thanks everyone for all of your contributions, help and feedback! The initial
LDA implementation has been merged, but there are many improvements which
remain to be done. I've put a list of JIRAs here
[https://issues.apache.org/jira/browse/SPARK-5572]
[~yuhaoyan] +1 for online LDA. (I made a JIRA for it.)
was (Author: josephkb):
Thanks everyone for all of your contributions, help and feedback! The initial
LDA implementation has been merged, but there are many improvements which
remain to be done. I've put a list of JIRAs here
[https://issues.apache.org/jira/browse/SPARK-5572]
[~yuhao yang] +1 for online LDA. (I made a JIRA for it.)
> parallel Latent Dirichlet Allocation (LDA) atop of spark in MLlib
> -----------------------------------------------------------------
>
> Key: SPARK-1405
> URL: https://issues.apache.org/jira/browse/SPARK-1405
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Xusen Yin
> Assignee: Joseph K. Bradley
> Priority: Critical
> Labels: features
> Fix For: 1.3.0
>
> Attachments: performance_comparison.png
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> Latent Dirichlet Allocation (a.k.a. LDA) is a topic model which extracts
> topics from text corpus. Different with current machine learning algorithms
> in MLlib, instead of using optimization algorithms such as gradient desent,
> LDA uses expectation algorithms such as Gibbs sampling.
> In this PR, I prepare a LDA implementation based on Gibbs sampling, with a
> wholeTextFiles API (solved yet), a word segmentation (import from Lucene),
> and a Gibbs sampling core.
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