Just to give an example, here's how I want to apply PCA to spectroscopy
experiments:
http://pdf.lookchem.com/pdf/32/f21930e7-09dc-4f70-ba24-f1edf8fdf02e.pdf
On Sat, Aug 2, 2014 at 3:28 PM, Adam Hughes <[email protected]> wrote:
> 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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>
>
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