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https://issues.apache.org/jira/browse/SPARK-7334?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14590711#comment-14590711
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Yu Ishikawa commented on SPARK-7334:
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[~sebalf], I'm very sorry for the delay of my response. And thank you for your
PR. I'm afraid I haven't taken a look at your PR. So I will see it in a few
days.
In my opinion, if your idea seems to be fit for the design I'm thinking, your
PR should be merged before creating LSH abstraction. And then we should change
or depreciating sometimes your implementation, if necessary. Anyway, I will
check it and get back to you by next Monday or Tuesday. If there seems to be no
problem, I'll ask any commiter to review your PR.
> 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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