[ 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: 2.1 > 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: 2.1 > > > 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. -- This message is automatically generated by JIRA. - You can reply to this email to add a comment to the issue online.