[SciPy-User] linear algebra: quadratic forms without linalg.inv
Robert Kern
robert.kern@gmail....
Sun Nov 1 23:19:50 CST 2009
On Mon, Nov 2, 2009 at 00:15, <josef.pktd@gmail.com> wrote:
> 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.
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.
--
Robert Kern
"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
More information about the SciPy-User
mailing list