Hi,
I was looking to the PCA and SparsePCA implementation of sklearn.
They are both based on SVD but I think that the nipals implementation of
the same algorithm can really increase the speed in some situations.
In particular with sparse PCA we usually use a small number of components
and so its speed can be increased using nipals to compute the initial
value of u,v (in the class dictionary learning).
Always on this road there is a nipals like algorithm for Sparse PCA. I have
already written a python implementation for this and should not be a
problem for me to integrate it with sklearn.
Is this considered useful for the community or is this off topic?
Are there others people already working on it?
Thanks,
Luca
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