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https://issues.apache.org/jira/browse/MATH-321?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12786250#action_12786250
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Jake Mannix commented on MATH-321:
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I'm in the process of porting my sparse SVD work from decomposer
(http://decomposer.googlecode.com) to Apache Mahout (see ticket MAHOUT-180 on
the mahout JIRA), which has both non-parallized stream-based sparse SVD (using
the generalized hebbian algorithm) as well as Hadoopified Lanczos and
probabalistic partial decomposition. These are designed to scale to tens of
millions of dimensions (the GHA version), and to billions of dimensions or more
(in the Hadoopified version - add more machines to your Hadoop cluster, and you
can go higher!).
> 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
>
> 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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