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 <chris@cellixis.com> 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?
>
