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From "Sebastian Alfers (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-7334) Implement RandomProjection for Dimensionality Reduction
Date Mon, 04 May 2015 11:32:06 GMT

     [ https://issues.apache.org/jira/browse/SPARK-7334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sebastian Alfers updated SPARK-7334:
------------------------------------
    Target Version/s:   (was: 1.3.1)

> 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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