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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14279274#comment-14279274
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Joseph K. Bradley edited comment on SPARK-1405 at 1/15/15 9:29 PM:
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I'll try out the statmt dataset if that will be easier for everyone to access.
UPDATE: Note: The statmt dataset is an odd one since each "document" is a
single sentence. I'll still try it since I could imagine a lot of users
wanting to run LDA on tweets or other short documents, but I might continue
with my previous tests first.
was (Author: josephkb):
I'll try out the statmt dataset if that will be easier for everyone to access.
> 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
> Priority: Critical
> 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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