Hi all,
I noticed that there is little interest in Mult-view analysis in sklearn.
The current implemented modules are PLS and CCA. The documentation has no
examples (I think) for them. I can add something on that direction and also
help in implementing KPLS,KCCA, Bi-linear models (BLM) and other mult-view
algorithms. I should also emphasize the code of PLS included in the library
is great, perfect if one needs to learn how the algorithm works, but for an
ordinary user it might need some more work to get inline with other modules
in the library.
If you are interested I would like to help on this line.
Regards
On Tue, Mar 26, 2013 at 9:31 AM, Mathieu Blondel <math...@mblondel.org>wrote:
> On Tue, Mar 26, 2013 at 3:28 PM, Gael Varoquaux
> <gael.varoqu...@normalesup.org> wrote:
>
> > * For matrix factorization to be useful in the context of recomender
> > systems, there needs to be an API for recomender systems. While I'd
> > love to see this, I am afraid that it might be premature and should
> > probably happen after the release of 1.0.
>
> It seems to me that inverse_transform would do the job:
> X_transformed = estimator.fit_transform(X) # X contains missing values
> X = estimator.inverse_transform(X_transformed) # missing values were
> imputed
>
> Mathieu
>
>
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