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From Dmitriy Lyubimov <>
Subject Re: SSVD for dimensional reduction + Kmeans
Date Thu, 09 Aug 2012 23:44:54 GMT
On Thu, Aug 9, 2012 at 4:34 PM, Pat Ferrel <> wrote:
> Quoth Grant Ingersoll:
>> To put this in bin/mahout speak, this would look like, munging some names and taking
liberties with the actual argument to be passed in:
>> bin/mahout svd (original -> svdOut)
>> bin/mahout cleansvd ...
>> bin/mahout transpose svdOut -> svdT
>> bin/mahout transpose original -> originalT
>> bin/mahout matrixmult originalT svdT -> newMatrix
>> bin/mahout kmeans newMatrix
> I'm trying to create a test case from testKmeansDSVD2 to use SSVDSolver. Does SSVD require
the EigenVerificationJob to clean the eigen vectors?


> if so where does SSVD put the equivalent of DistributedLanczosSolver.RAW_EIGENVECTORS?
Seems like they should be in V* but SSVD creates V so should I transpose V* then run it through
the EigenVerificationJob?

SSVD is SVD, meaning it produces U and V with no further need to clean that

> I get errors when I do so trying to figure out if I'm on the wrong track.

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