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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14273551#comment-14273551
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Valeriy Avanesov commented on SPARK-1405:
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[~josephkb], I've read your proposal and I suggest to consider Stochastic
Gradient Langevin Dynamics [1]. It was shown be ~100 times faster than Gibbs
sampling [2]. Though, I'm not sure if it's implementable in terms of RDD.
[1]
http://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf
[2] http://www.ics.uci.edu/~sungjia/icml2014_dist_v0.2.pdf
> 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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