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https://issues.apache.org/jira/browse/SPARK-6227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14357354#comment-14357354
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Joseph K. Bradley commented on SPARK-6227:
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I was about to---but then I realized this is a much bigger task than it appears
since it will require writing Python wrappers for the various distributed
matrix types. I just made this a subtask of another JIRA. Could you take a
look at the parent JIRA and the distributed matrices code and figure out a good
piece of the work to start with? Hopefully we can break the work into pieces
in a natural way. Thanks!
> PCA and SVD for PySpark
> -----------------------
>
> Key: SPARK-6227
> URL: https://issues.apache.org/jira/browse/SPARK-6227
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib, PySpark
> Affects Versions: 1.2.1
> Reporter: Julien Amelot
>
> The Dimensionality Reduction techniques are not available via Python (Scala +
> Java only).
> * Principal component analysis (PCA)
> * Singular value decomposition (SVD)
> Doc:
> http://spark.apache.org/docs/1.2.1/mllib-dimensionality-reduction.html
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