On Fri, Mar 6, 2015 at 11:09 AM, Luca Puggini <lucapug...@gmail.com> wrote:
> Hi,
> It seems to me that you are discussing topics that can be introduced in
> sklearn with GSoC.
>
> I use sklearn quiet a lot and there are a couple of things that I really
> miss in this library:
>
> 1- Nipals PCA.
> The current version of PCA is too low for high dimensional dataset.
> Suppose to have p=10000 variables and be interested in only the first 10
> principal components. In a situation like this nipals PCA is much more
> efficient. Also other algorithms like PLS can increase their computational
> performance with nipals PCA
>
>
PCA does an SVD, whose complexity depends on the shorter side of the
matrix. If you have n=100, p=10000, the complexity is O(n^2 * p). However,
if both dimensions are high, it is true that a decomposition that only
calculates the required number of components becomes necessary.
RandomizedPCA
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/pca.py#L468
does this using random projection, nipals would be an alternative.
PLS already uses nipals
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/pls_.py#L22
In the context of a refactoring of PLS/CCA, it there could also be an
evaluation of the existing nipals in PCA.
2- Something to rank the variables
> At the moment it seems to me that the only way to rank the variables is
> the Random Forest importance. This method is known to be very very biased.
> I suggest something like the method implemented in the R library party.
>
>
Could you elaborate?
>
> I hope that these comments can help.
> I may decide to apply for GSoC as well :-)
>
> Best,
> Luca
>
>
>
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