[jira] [Commented] (SPARK-6227) PCA and SVD for PySpark

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-6227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14428301#comment-14428301 ] 

Xiangrui Meng commented on SPARK-6227:
--------------------------------------

[~MeethuMathew] I agree with Joseph that we should add distributed linear algebra data models in Python first before we wrap PCA and SVD. I will link the JIRAs and it would be great if you can help on those JIRAs first.

> 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



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org