2013/10/15 Olivier Grisel <olivier.gri...@ensta.org>: > 2013/10/15 Alexandre Gramfort <alexandre.gramf...@telecom-paristech.fr>: >>> I did find the part in coordinate_descent.py where alpha_max is chosen, but >>> I don't fully understand the reasoning behind it: >>> >>> alpha_max = np.abs(Xy).max() / (n_samples * l1_ratio) >> >> it can be derived from the KKT optimality conditions of the Lasso problem. > > Would be great to add a link to an online reference or the derivation > somewhere in the doc.
Also is it impacted by the lack of greedy data-centering in the sparse case? It seems it does to me. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ October Webinars: Code for Performance Free Intel webinars can help you accelerate application performance. Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from the latest Intel processors and coprocessors. See abstracts and register > http://pubads.g.doubleclick.net/gampad/clk?id=60135031&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
