Hi,
First at all, I would like to warmly thank the scikit developer community with
providing us with such a high quality ML library: it really became an amazing
piece of scientific software.
I have a comment concerning the online documentation on Matrix Factorization
Problems.
(I use this mailing list because I could not find in your online howto, what is
the best channel to communicate documentation issues.Apologies if this email is
considered as spam in this mailing list !)
On the webpage 2.5. Decomposing signals in components (matrix factorization
problems) — scikit-learn 0.18.2 documentation
We can read at 2.5.1.5. Sparse principal components analysis
but a bit further, at 2.5.3.2. Generic dictionary learning, we can read
The notations are obviously inconsistent as U and V have been interchanged some
how.
Two extra (less important) corrections could probably improve even further the
clarity for the reader:1. Sticking to a single upper bound limit (either
n_components or n_atoms)2. Specifying whether V_k are columns or rows (maybe
using a notation à la Matlab/Numpy: V_{:,k} or V_{k,:})
Kind regards,
Axel BREUER
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