[jira] [Reopened] (SPARK-14022) What about adding RandomProjection to ML/MLLIB as a new dimensionality reduction algorithm?

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

     [ https://issues.apache.org/jira/browse/SPARK-14022?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

zhengruifeng reopened SPARK-14022:
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There may need some discuss on whether to add RandomProjection or Not. 

> What about adding RandomProjection to ML/MLLIB as a new dimensionality reduction algorithm?
> -------------------------------------------------------------------------------------------
>
>                 Key: SPARK-14022
>                 URL: https://issues.apache.org/jira/browse/SPARK-14022
>             Project: Spark
>          Issue Type: Brainstorming
>            Reporter: zhengruifeng
>            Priority: Minor
>
> What about adding RandomProjection to ML/MLLIB as a new dimensionality reduction algorithm?
> RandomProjection (https://en.wikipedia.org/wiki/Random_projection) reduces the dimensionality by projecting the original input space on a randomly generated matrix. 
> It is fully scalable, and runs fast (maybe fastest).
> It was implemented in sklearn (http://scikit-learn.org/stable/modules/random_projection.html)
> I am be willing to do this, if needed.



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