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Posted to dev@mahout.apache.org by "Ted Dunning (JIRA)" <ji...@apache.org> on 2011/08/17 17:00:30 UTC
[jira] [Commented] (MAHOUT-771) Random Projection using sampled
values
[ https://issues.apache.org/jira/browse/MAHOUT-771?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13086365#comment-13086365 ]
Ted Dunning commented on MAHOUT-771:
------------------------------------
I think so.
It is a useful idea and I will include a variant in the SSVD refresh that I have coming up. I think that murmurhash is over-kill here and a simple prime number congruential approach versus the indexes is likely just as good.
> 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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