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Posted to issues@spark.apache.org by "Guoqiang Li (JIRA)" <ji...@apache.org> on 2014/09/01 16:26:20 UTC
[jira] [Comment Edited] (SPARK-1405) parallel Latent Dirichlet
Allocation (LDA) atop of spark in MLlib
[ https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14117453#comment-14117453 ]
Guoqiang Li edited comment on SPARK-1405 at 9/1/14 2:25 PM:
------------------------------------------------------------
Hi all
[PR 1983|https://github.com/apache/spark/pull/1983] is OK to review.
was (Author: gq):
Hi all
[PR 1983|https://github.com/apache/spark/pull/1983] is ready to review.
> 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: Xusen Yin
> Labels: features
> 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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