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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2016/03/19 10:40:33 UTC

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

zhengruifeng created SPARK-14022:
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             Summary: 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: Question
            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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