[SciPy-User] linear algebra: quadratic forms without linalg.inv

josef.pktd@gmai... josef.pktd@gmai...
Sun Nov 1 23:15:41 CST 2009

On Mon, Nov 2, 2009 at 12:33 AM, Sturla Molden <sturla@molden.no> wrote:
> josef.pktd@gmail.com skrev:
>> if we have enough multicollinearity that numerical
>> precision matters, then we are screwed anyway and have to rethink the
>> data analysis or the model, or do a pca.
> And PCA has nothing to do with SVD, right?

> Or ... what what would you call a procesure that takes your data,
> subtracts the mean, and does an SVD?

All the explanations I read where in terms of eigenvalue decomposition
and not with SVD. I'm pretty good in removing negative eigenvalues
when I'm supposed to have a positive definite matrix, but SVD has too
many parts.

(Besides I don't like pca for regression, and I'm still struggling how to
do partial least squares with SVD.)


> :-D
> Sturla
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