2011/12/7 Gael Varoquaux <[email protected]>: > On Tue, Dec 06, 2011 at 07:43:26PM -0500, David Warde-Farley wrote: >> I think that scaling by n_samples makes sense in the supervised learning >> context (we often do the equivalent thing where we take the mean, rather than >> the sum, over the unregularized training objective, making the regularization >> invariant to the size of the training set), however there is a disconnect >> between the dictionary learning notion of n_samples and the supervised >> estimator notion of n_samples, and the conflation of these two because one >> can be implemented by the other. > > +1. We may need to have a different convention in Lasso and > sparse_encode. This should be well documented.
+1 as Vlad said we just need to divide alpha by the size of the dictionary before calling Lasso from the sparse_encode function to nullify the normalization for this specific case only. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Cloud Services Checklist: Pricing and Packaging Optimization This white paper is intended to serve as a reference, checklist and point of discussion for anyone considering optimizing the pricing and packaging model of a cloud services business. Read Now! http://www.accelacomm.com/jaw/sfnl/114/51491232/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
