Support for Sparse (Thin) SVD
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Key: MATH-321
URL: https://issues.apache.org/jira/browse/MATH-321
Project: Commons Math
Issue Type: New Feature
Reporter: David Jurgens
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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