A few notes: * Online and parallelisation are different things, both interesting and to keep in mind, but that should not be confused.
* For non-negative matrix factorization, Julien Mairal's algorithm for online dictionary learning can also be used (see the JMLR paper). It needs a small modification compared to what we currently have, but it shouldn't be too much work. * For matrix factorization to be useful in the context of recomender systems, there needs to be an API for recomender systems. While I'd love to see this, I am afraid that it might be premature and should probably happen after the release of 1.0. G ------------------------------------------------------------------------------ Own the Future-Intel® Level Up Game Demo Contest 2013 Rise to greatness in Intel's independent game demo contest. Compete for recognition, cash, and the chance to get your game on Steam. $5K grand prize plus 10 genre and skill prizes. Submit your demo by 6/6/13. http://p.sf.net/sfu/intel_levelupd2d _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
