Details
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New Feature
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Status: Closed
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Major
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Resolution: Fixed
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None
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None
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Description
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.
Attachments
Attachments
Issue Links
- is blocked by
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MAHOUT-790 Redundancy in Matrix API, view or get?
- Closed
- is depended upon by
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MAHOUT-797 MapReduce SSVD: provide alternative B-pipeline per B=R' ^{-1} Y'A
- Closed