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List:       jakarta-commons-dev
Subject:    [jira] Commented: (MATH-321) Support for Sparse (Thin) SVD
From:       "Luc Maisonobe (JIRA)" <jira () apache ! org>
Date:       2009-12-31 18:03:29
Message-ID: 150632491.1262282609433.JavaMail.jira () brutus ! apache ! org
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    [ https://issues.apache.org/jira/browse/MATH-321?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12795630#action_12795630 \
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Luc Maisonobe commented on MATH-321:
------------------------------------

A partial fix as been committed in subversion repository as of r894908.
The current implementation computes either the compact SVD (considering only positive \
singular values) or the truncated SVD (considering a user-specified maximal number of \
singular values). The issue is however not completely solved yet as the underlying \
eigendecomposition still computes all eigenvalues,. The SVD upper layer only \
truncates this computation afterwards. This means lots of things are computed just to \
be discarded later. I'll take care of this shortly.
Also note that this implementation still considers only dense matrices, not sparse \
ones. Any contributions for sparse SVD is welcome!

> 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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