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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2016/04/10 08:50:25 UTC
[jira] [Updated] (SPARK-14022) What about adding RandomProjection
to ML/MLLIB as a new dimensionality reduction algorithm?
[ https://issues.apache.org/jira/browse/SPARK-14022?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhengruifeng updated SPARK-14022:
---------------------------------
Issue Type: Brainstorming (was: Question)
> 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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