Subject: Computing SVD Of "Large Sparse Data"


I would push for SSVD as well if you want a real SVD.

Also, I don't think that you lose information about which vectors are which
(or as Jake put it "what they mean").  The stochastic decomposition gives a
very accurate estimate of the top-k singular vectors.  It does this by using
the random projection to project the top singular vectors into a sub-space
and then correcting the results obtained back into the original space.  This
is not the same as simply doing the decomposition on the random projection
and then using that decomposition.

On Fri, Jun 3, 2011 at 8:16 PM, Eshwaran Vijaya Kumar <
[EMAIL PROTECTED]> wrote: