Hey Ted, I finally had time to get back to this. This is definitely bringing back some memories :) I hope you have room for (hopefully) one more question.
So, I have been studying Simon Funk's incremental SVD approach (this is implemented in ExpectationMaximizationSVDFactorizer). In this method, the singular values are folded in to the left and right matrices: A = U * sqrt(d) * sqrt(d) * VT = U' * V'T So, in this case, inverse(V'T) = V * d^-1/2 Whatever the case, my question is the same: given U' and V'T, I am failing to see an elegant (i.e. trivial) solution to extracting the singular values. I was hoping you could help me out. Thanks again, Chris On Feb 25, 2011, at 2:29 PM, Ted Dunning wrote: > Yes. That affects things. The key is that inverse(diag(d_1 ... d_n)) = > diag(1/d_1 ... 1/d_n) > > that means that inverse(D V') = V inverse(D). If you have X' = DV' you need > to compute inverse(X') = X D^-2 > > On Fri, Feb 25, 2011 at 1:25 PM, Chris Schilling <[email protected]> wrote: > One more linear algebra question. So, does this still hold when the diag(d) > matrix is multiplied through the right hand side? Is that an affect I should > worry about when trying to compute u? >
