You are viewing a plain text version of this content. The canonical link for it is here.

Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/04/12 09:59:41 UTC

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

    [ https://issues.apache.org/jira/browse/SPARK-6227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15965632#comment-15965632 ] 

Apache Spark commented on SPARK-6227:
-------------------------------------

User 'MLnick' has created a pull request for this issue:
https://github.com/apache/spark/pull/17621

> PCA and SVD for PySpark
> -----------------------
>
>                 Key: SPARK-6227
>                 URL: https://issues.apache.org/jira/browse/SPARK-6227
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib, PySpark
>    Affects Versions: 1.2.1
>            Reporter: Julien Amelot
>            Assignee: Manoj Kumar
>
> 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
(v6.3.15#6346)

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