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https://issues.apache.org/jira/browse/MAHOUT-792?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13091902#comment-13091902
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Dmitriy Lyubimov commented on MAHOUT-792:
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This looks like a very promising shortcut for MR operationally.
AFAICT, you suggest to compute Y'Y and then derive R^-1 out of it and use it in
another pass to derive B. Y'Y is indeed operationally much easier to accumulate
and produce with MR in the same projection step.
One thing that i don't understand is how you are proposing to derive this R^-1.
To do Cholesky on Y'Y, take transpose and inverse and declare it R^-1?
> Add new stochastic decomposition code
> -------------------------------------
>
> Key: MAHOUT-792
> URL: https://issues.apache.org/jira/browse/MAHOUT-792
> Project: Mahout
> Issue Type: New Feature
> Reporter: Ted Dunning
> Attachments: MAHOUT-792.patch, MAHOUT-792.patch, sd-2.pdf
>
>
> I have figured out some simplification for our SSVD algorithms. This
> eliminates the QR decomposition and makes life easier.
> I will produce a patch that contains the following:
> - a CholeskyDecomposition implementation that does pivoting (and thus
> rank-revealing) or not. This should actually be useful for solution of large
> out-of-core least squares problems.
> - an in-memory SSVD implementation that should work for matrices up to
> about 1/3 of available memory.
> - an out-of-core SSVD threaded implementation that should work for very
> large matrices. It should take time about equal to the cost of reading the
> input matrix 4 times and will require working disk roughly equal to the size
> of the input.
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