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From "Phil Steitz (JIRA)" <j...@apache.org>
Subject [jira] Updated: (MATH-321) Support for Sparse (Thin) SVD
Date Sat, 03 Apr 2010 03:04:27 GMT

     [ https://issues.apache.org/jira/browse/MATH-321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Phil Steitz updated MATH-321:
-----------------------------

    Fix Version/s:     (was: 2.1)
                   2.2

> 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: 2.2
>
>
> 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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