[jira] [Commented] (SPARK-7334) Implement RandomProjection for Dimensionality Reduction

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-7334?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15603030#comment-15603030 ] 

Joseph K. Bradley commented on SPARK-7334:
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[~sebalf] I'm sorry we weren't able to get your PR in.  I do appreciate your work on this!  Looking back, I believe the functionality in this JIRA should be a subset of what is in the PR for [SPARK-5992], so I'll go ahead and close this JIRA issue.  If you have time, feedback on the current PR for [SPARK-5992] would be very valuable.  Thanks very much.

> Implement RandomProjection for Dimensionality Reduction
> -------------------------------------------------------
>
>                 Key: SPARK-7334
>                 URL: https://issues.apache.org/jira/browse/SPARK-7334
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Sebastian Alfers
>            Priority: Minor
>
> Implement RandomProjection (RP) for dimensionality reduction
> RP is a popular approach to reduce the amount of data while preserving a reasonable amount of information (pairwise distance) of you data [1][2]
> - [1] http://www.yaroslavvb.com/papers/achlioptas-database.pdf
> - [2] http://people.inf.elte.hu/fekete/algoritmusok_msc/dimenzio_csokkentes/randon_projection_kdd.pdf
> I compared different implementations of that algorithm:
> - https://github.com/sebastian-alfers/random-projection-python



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