Deepak,
Thanks for getting this discussion started. I also had some similar
concerns. There are some new applications in spectroscopy that use the
principle components on spectral data; however, in those systems, the
centering is done along the feature axis. Thus, my dataset is shaped like
your svd_T() example. This really helps me keep things straight, so I'll
probably put a reference to this thread in any PCA tutorials I put into my
spectroscopy package. Thanks also to the rest of the participants in this
thread for explaining some of this nuance that is often taken for granted
in PCA.
I read through the thread, and am still not 100% sure what the best
approach for the SVD_T() case is. For data of shape samples X features,
and we want to do PCA along the features, is SVD_T() sufficient, or should
I wait for the incremental PCA PR?
On Thu, Jul 31, 2014 at 11:27 AM, Deepak Pandian <[email protected]>
wrote:
> On Thu, Jul 31, 2014 at 8:50 PM, Michael Eickenberg
> <[email protected]> wrote:
> > Coming soon :) https://github.com/scikit-learn/scikit-learn/pull/3285
>
> That looks cool. I will look at it.
>
>
>
> --
> With Regards,
> Deepak Pandian
> "Deconstructing world one piece at a time"
>
>
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