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https://issues.apache.org/jira/browse/MAHOUT-376?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dmitriy Lyubimov updated MAHOUT-376:
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Attachment: QR decomposition for Map.pdf
I am currently working to drive the working prototype the version with
standalone thin QR step which would be memory independent of num of rows in A
and would result into BBt eigensolution of (k+p)x(k+p) dimensionality only, the
rest being driven by Map Reduce. I've got single stream version working
seemingly well, here's the update on this WIP. Although in the end i am not
sure it would offer any real-life improvement, as it would seem to require a
second pass over A as it is not possible to finish Q^t x B computation in
single step with this approach.
Still, after 2 passes, we should be done with eigen values (and perhaps
(k+p)x(k+p) dimensionality for eigensolver input would allow us to increase
oversampling p somewhat, hence precision). Hard to see from here yet though.
Additional (optional) MR steps would only be needed if U or V is or both are
desired.
> Implement Map-reduce version of stochastic SVD
> ----------------------------------------------
>
> Key: MAHOUT-376
> URL: https://issues.apache.org/jira/browse/MAHOUT-376
> Project: Mahout
> Issue Type: Improvement
> Components: Math
> Reporter: Ted Dunning
> Assignee: Ted Dunning
> Fix For: 0.5
>
> Attachments: MAHOUT-376.patch, Modified stochastic svd algorithm for
> mapreduce.pdf, QR decomposition for Map.pdf, sd-bib.bib, sd.pdf, sd.pdf,
> sd.pdf, sd.pdf, sd.tex, sd.tex, sd.tex, sd.tex, Stochastic SVD using
> eigensolver trick.pdf
>
>
> See attached pdf for outline of proposed method.
> All comments are welcome.
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