Thanks!
On Sat, Aug 2, 2014 at 4:17 PM, Michael Eickenberg <
[email protected]> wrote:
> As far as I understand, Deepak was trying to obtain the same results with
> both methods. His svd_t method centered along the feature axis, but he also
> fed it with X.T, making it center along samples again. The surprising part
> was that the last singular vectors did not correspond, and if I am not
> mistaken, this is due to the centering operation and multiplicity of the
> 0-eigenspace. So what you are trying to do is slightly different: Using
> svd_t on X or using pca on X.T.
>
> Michael
>
>
> On Sat, Aug 2, 2014 at 9:32 PM, Adam Hughes <[email protected]>
> wrote:
>
>> 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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