[jira] [Comment Edited] (SPARK-1405) parallel Latent Dirichlet Allocation (LDA) atop of spark in MLlib

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

    [ https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14073013#comment-14073013 ] 

lukovnikov edited comment on SPARK-1405 at 7/24/14 9:10 AM:
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@Isaac, I think it's at https://github.com/yinxusen/spark/blob/lda/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala and here (https://github.com/apache/spark/pull/476/files) for the other changed files as well


was (Author: lukovnikov):
@Isaac, I think it's at https://github.com/yinxusen/spark/blob/lda/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala

> 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: Improvement
>          Components: MLlib
>    Affects Versions: 1.1.0
>            Reporter: Xusen Yin
>            Assignee: Xusen Yin
>              Labels: features
>             Fix For: 0.9.0
>
>   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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