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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/11/10 23:54:34 UTC
[jira] [Updated] (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:all-tabpanel ]
Xiangrui Meng updated SPARK-1405:
---------------------------------
Target Version/s: 1.3.0 (was: 1.2.0)
> 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: Guoqiang Li
> Labels: features
> 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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