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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14131299#comment-14131299
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Xiangrui Meng commented on SPARK-1405:
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[~xusen] and [~gq] Thanks for working on LDA! The major feedback of your
implementations is the how models are stored.
[~josephkb] and I had an offline discussion with Evan and Joey (AMPLab) on
LDA's interface and implementation. For the input data, we recommend `RDD[(Int,
Vector)]`, where each pair consists of the document id and its word
distribution, which may come from a text vectorizer. For the output model,
because the LDA model is huge (W*K + D*K), where W is the number of words, D is
the number of documents, and K is the number of topics, we should store the
model distributively for better scalability, e.g., in RDD[(Int, Vector)], or
using Long for ids. Joey already had an LDA implementation using GraphX:
https://github.com/jegonzal/graphx/blob/LDA/graph/src/main/scala/org/apache/spark/graph/algorithms/TopicModeling.scala
With GraphX, we can treat documents and words as graph nodes and topic
assignments as edges. The code is easy to understand.
There is also a paper describing a distributed implementation of LDA on Spark
that uses a DGSD-like partitioning of the doc-word matrix:
http://jmlr.org/proceedings/papers/v36/qiu14.pdf
Anyone interested in helping test those implementations?
> 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: Xusen Yin
> Labels: features
> 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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