Java implementation of singular value decomposition for large sparse matrices

Question

I'm just wondering if anyone out there knows of a java implementation of singular value decomposition (SVD) for large sparse matrices? I need this implementation for latent semantic analysis (LSA).

I tried the packages from UJMP and JAMA but they choke when the number of row >= 1000 and col >= 500. If anyone can point me to psuedocode or something out there, that would be greatly appreciated.

Answer 1

There's a list of Java numerical libraries at Wikipedia. The NIST library, which is quite good, unfortunately does not deal with sparse matrices. I'm not too familiar with the other packages. You might take a look at Colt; it's also quite high quality and does handle sparse matrices for some operations; I don't know about SVD, although I imagine it does. I've also heard that UJMP is also worth a look.

EDIT: Sorry to hear that UJMP doesn't handle your problem. I had heard that it was worth a look.