[Numpy-discussion] finding eigenvectors etc
Wed Feb 20 02:27:00 CST 2008
The vectors that you used to build your covariance matrix all lay in or close to
a 3-dimensional subspace of the 4-dimensional space in which they were
represented. So one of the eigenvalues of the covariance matrix is 0, or close
to it; the matrix is singular. Condition is the ratio of the largest eigenvalue
to the smallest, large values can be troublesome. Here it is ~1e17, which is
the dynamic range of doubles. Which means that the value you observe for the
smallest eigenvaulue is just the result of rounding errors.
On Wed, 20 Feb 2008, email@example.com wrote:
>> Different implementations follow different conventions as to which
>> is which.
> thank you for the replies ..the reason why i asked was that the most
> significant eigenvectors ( sorted according to eigenvalues) are later
> used in calculations and then the results obtained differ in java and
> python..so i was worried as to which one to use
>> Your matrix is almost singular, is badly conditionned,
> Mathew, can you explain that..i didn't quite get it..
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