On Sun, 29 Feb 2004, Philip Warner wrote:
> My understanding of SVD is that, for A an mxn matrix, m > n: > > A = UWV* > > where W is square root diagonal eigenvalues of A*A extended with zero > valued rows, and U and V are the left & right eigen vectors of A. But this > does not seem to be strictly true and seems to require specific > eigenvectors, and I am not at all sure how these are computed.
(A %*% t(A) is required, BTW.) That is not the definition of the SVD. It is true that U are eigenvectors of A %*% t(A) and V of t(A) %*% A, but that does not make them left/right eigenvectors of A (unless that is your private definition).
Sorry, that should have read 'left & right singular vectors', and I'm beginning to suspect that they are only the starting point for deriving the singular vectors (based on http://www.cs.utk.edu/~dongarra/etemplates/node191.html)
...cut...Since eigenvectors are not unique, it does mean that you cannot reverse the process, as you seem to be trying to do.
> > which seems a little off the mark.
It is not expected to work.
Maybe not by you... 8-}
There is no rule: the SVD is computed by a different algorithm.
So I assume my approach will not give me the singular vectors, and I need a different way of deriving them, is that right?
Thanks for your help, it is much appreciated.
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