I am planning to work on NIPALS after the 0.15 sklearn release - there are
several good papers I will try to work with and implement.
Simple, high level description:
http://www.vias.org/tmdatanaleng/dd_nipals_algo.html
Simple MATLAB (will start with this first likely):
http://www.cdpcenter.org/files/plsr/nipals.html
Parallel Gram-Schmidt (seems pretty great, if the easy version needs
speed): http://arxiv.org/pdf/0811.1081.pdf
Do you have other resources for implementation details or implementations
that you have used in the past? I think there was an old PR or issue for it
somewhere on GitHub, but I can't seem to find the discussion.
I have not looked at the PLS code before - maybe someone more familiar can
comment on your previous comment w.r.t. covariance vs. correlation.
On Thu, Jun 5, 2014 at 2:08 AM, Luca Puggini <lucapug...@gmail.com> wrote:
>
> Hi,
> I was looking to the code in the cross-decomposition section. In PLS the
> PCs are computed in order to maximize the correlation between X and y and
> not to maximize the covariance of X.
>
> def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06,
> norm_y_weights=False):
> """Inner loop of the iterative NIPALS algorithm.
>
> Provides an alternative to the svd(X'Y); returns the first left and right
> singular vectors of X'Y. See PLS for the meaning of the parameters. It
> is
> similar to the Power method for determining the eigenvectors and
> eigenvalues of a X'Y.
> """
>
> In my opinion a standard implementation of Nipals PCA would be useful for
> sklearn and probably easy to develop
>
> Best,
>
> Luca
>
>
> On Mon, Jun 02, 2014 at 12:27:34AM +0100, Luca Puggini wrote:
>> > This is a good alternative to SVD and it is much faster in situations
>> where we
>> > have a lot of variables and we are interested only in a small number of
>> > components.
>> > This is a well known and tested algorithm and I was actually surprised
>> when I
>> > discovered that it is not in sklearn.? (Maybe it has been replaced by a
>> faster
>> > alternative?)
>>
>> It is in scikit-learn, I believe, but written in such a way that nobody
>> finds it are uses it (there are a few lessons to be learned there :) ):
>> inside sklearn.cross_decomposition, you'll find some NIPALS.
>>
>> G
>>
>>
>>
>
>
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