OK, that may explain it:
You have less samples than features. You mean center your data. This means
that you are adding one singular value of 0. The last component corresponds
to this 0 singular value. But actually the space of the 0 singular value is
8 dimensional and can be arbitrarily spanned by any orthonormal system. You
are only looking at one of these 8 vectors. All 8 of them together will
span the same space whether you use X or X.T. But since this decomposition
is arbitrary, it may find different vectors on different runs, e.g. with X
and X.T.
Hope this is it :)
Michael
On Thu, Jul 31, 2014 at 4:25 PM, Deepak Pandian <[email protected]>
wrote:
> On Thu, Jul 31, 2014 at 7:49 PM, Kyle Kastner <[email protected]>
> wrote:
> > It looks like the transpose may make the system under-determined. If you
> try
> > with
> >
> > X = np.random.randn(*X.shape)
>
> Sorry I didnt quite get what to do here, I tried,
> shape=(3,10)
> X = rng.randn(*shape)
>
> and it doesn't change anything.
>
> --
> With Regards,
> Deepak Pandian
> "Deconstructing world one piece at a time"
>
>
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