Thanks Alexandre,
I'm not sure that's the case for me. Our data is wavelength in rows and
time in columns. Say an experiment takes 500 timepoints. I'd like to run
PCA along the time variable (axis=1) and be able to represent my data as a
wavelength vs. k_components matrix, where n << k_timepoints. I know this
is an unconventional implementation, but in this case, do you still think
it's axis=0?
On Tue, Jul 22, 2014 at 3:29 AM, Alexandre Gramfort <
alexandre.gramf...@telecom-paristech.fr> wrote:
> hi,
>
> proper PCA is run on centered data (axis=0) otherwise it's a truncated SVD.
> I seams you want a PCA on X.T (X transposed).
>
> HTH
> Alex
>
>
> On Tue, Jul 22, 2014 at 3:14 AM, Adam Hughes <hughesada...@gmail.com>
> wrote:
> > Hi,
> >
> > I'm really enjoying scikit learn and looking to add a lite version of
> PCA to
> > some programs I'm working on, derived mostly from the decomposition.PCA
> > class. I have a few quick questions, and really would appreciate some
> help
> > from the experts.
> >
> > First, the PCA class is designed to operate on data of dimensions:
> > n_samples, n_features
> >
> > And transforms to:
> > n_samples, n_components
> >
> > Our data is of the form:
> > n_features, n_samples
> >
> > However we are doing correlation spectroscopy, so we actually want to
> treat
> > the samples as the pertubation axis. Therefore, our components should
> end
> > up with dimensions:
> >
> > n_features, n_components
> >
> > The dimensions sklearn PCA is returning looks fine, but I'm worried about
> > the mean_centering operaiton. I want our data to be centered around
> axis=1.
> > I'm wondering if I change just the mean centering axis, if you think
> > anything else in the analysis will break or go awry? Or if you think
> that's
> > safe, given the dimensions of my data?
> >
> > And finally, I'm curious wjat it means to run PCA on data that is not
> > mean-centered. Does it lose all interpretation, or is it sill a
> > valid/sometimes performed operation?
> >
> > Thanks guys!
> >
> >
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