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https://issues.apache.org/jira/browse/MATH-321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Luc Maisonobe updated MATH-321:
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Fix Version/s: 3.0
(was: 2.2)
SVD has been once again revamped for 2.1 and will probably be changed again
soon.
We need to stabilize the implementation of regular SVD before working on this.
Postponing to 3.0
> Support for Sparse (Thin) SVD
> -----------------------------
>
> Key: MATH-321
> URL: https://issues.apache.org/jira/browse/MATH-321
> Project: Commons Math
> Issue Type: New Feature
> Reporter: David Jurgens
> Fix For: 3.0
>
>
> Current the SingularValueDecomposition implementation computes the full SVD.
> However, for some applications, e.g. LSA, vision applications, only the most
> significant singular values are needed. For these applications, the full
> decomposition is impractical, and for large matrices, computationally
> infeasible. The sparse SVD avoids computing the unnecessary data, and more
> importantly, has significantly lower computational complexity, which allows
> it to scale to larger matrices.
> Other linear algebra implementation have support for the sparse svd. Both
> Matlab and Octave have the svds function. C has SVDLIBC. SVDPACK is also
> available in Fortran and C. However, after extensive searching, I do not
> believe there is any existing Java-based sparse SVD implementation. This
> added functionality would be widely used for any pure Java application that
> requires a sparse SVD, as the only current solution is to call out to a
> library in another language.
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