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https://issues.apache.org/jira/browse/SPARK-7334?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14588950#comment-14588950
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Joseph K. Bradley commented on SPARK-7334:
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I linked this issue to the LSH issue since LSH can use random projections.
Thinking more about it, random projection should probably be its own feature
transformer since it's often not thought of as an LSH method.
So backpedaling partway...the main thing to check w.r.t. [SPARK-5992] is
whether an LSH method based on random projections can use your code.
[~yuu.ishik...@gmail.com] If there are design choices affecting your LSH plans,
can you please comment here?
Thanks!
People are catching up still after all of the release QA...but reviews should
resume in full force before long.
> 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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