[SciPy-User] linear algebra: quadratic forms without linalg.inv
Sun Nov 1 23:19:50 CST 2009
On Mon, Nov 2, 2009 at 00:15, <firstname.lastname@example.org> wrote:
> On Mon, Nov 2, 2009 at 12:33 AM, Sturla Molden <email@example.com> wrote:
>> firstname.lastname@example.org 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.
Eigenvalues of the covariance matrix. The SVD gives you eigenvalues of
the covariance matrix directly from the demeaned data matrix without
explicitly forming the covariance matrix.
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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