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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14298351#comment-14298351
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Guoqiang Li edited comment on SPARK-1405 at 1/30/15 8:34 AM:
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Here is a sampling faster branch(work in progress):
https://github.com/witgo/spark/tree/lda_MH
[It's|https://github.com/witgo/spark/tree/lda_MH] computational complexity is
O(log(K)) K is the number of topic
[#2388|https://github.com/apache/spark/pull/2388]'s computational complexity is
O(log(K)+ Nkd) , K is the number of topic and Ndk
is the number of tokens in document d that are assigned to topic k
was (Author: gq):
Here is a sampling faster branch(work in progress):
https://github.com/witgo/spark/tree/lda_MH
[It's|https://github.com/witgo/spark/tree/lda_MH] computational complexity is
O(log(K)) K is the number of topic
[#2388|https://github.com/apache/spark/pull/2388]'s computational complexity is
O(log(K)) + Nkd, K is the number of topic and Ndk
is the number of tokens in document d that are assigned to topic k
> 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
> 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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