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https://issues.apache.org/jira/browse/MAHOUT-771?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13087137#comment-13087137
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Dmitriy Lyubimov commented on MAHOUT-771:
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I think SSVD in general (in the form it exists) can benefit from improvements
in flops since it feels somewhat cpu-bound.
However the projecting code is smaller part of it, it is sort of linear to the
input size (number of rows, anyway), whereas bottleneck is QR computations
which have a quadratic component to it and it quickly overshadows any
projection expenses as input size grows.
> Random Projection using sampled values
> --------------------------------------
>
> Key: MAHOUT-771
> URL: https://issues.apache.org/jira/browse/MAHOUT-771
> Project: Mahout
> Issue Type: New Feature
> Components: Math
> Reporter: Lance Norskog
> Priority: Minor
> Attachments: RandomProjector.patch, RandomProjectorBenchmark.java
>
>
> Random Projection implementation which follows two deterministic guarantees:
> # The same data projected multiple times produces the same output
> # Dense and sparse data with the same contents produce the same output
> Custom class that does Random Projection based on Johnson-Lindenstrauss. This
> implementation uses Achlioptas's results, which allow using method other than
> a full-range random multiplier per sample:
> * use 1 random bit to add or subtract a sample to a row sum
> * use a random value from 1/6 to add (1/6), subtract (1/6), or ignore (4 out
> of 6) a sample to a row sum
> Custom implementations for both dense and sparse vectors are included. The
> sparse vector implementation assumes the active values will fit in memory.
> An implementation using full-range random multipliers made by
> java.util.Random is included for reference/research.
> *Database-friendly random projections: Johnson-Lindenstrauss with binary
> coins*
> _Dimitris Achlioptas_
> [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.84.4546&rep=rep1&type=pdf]
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