Hi all, During the PyCon sprint I kept digging into the NMF and specifically ways to solve each sub-iteration. It became clear that the alternating NLS approach finds good reconstructions and converges well, but the NLS solving step is critical and must be optimized.
I have started looking into different ways to solve multi-target NLS problems. This is very much a work in progress but I wanted to share quickly so that I can get your feedback. Check out the notebook here: http://nbviewer.ipython.org/7224672 Adding "L1" (elementwise) regularization makes L-BFGS-B converge much quicker. This is cool because for NMF such a penalty has other advantages. I will add the projected gradient solver in pure python that we have and that seems to be very fast for larger n_targets. Cheers, Vlad ------------------------------------------------------------------------------ Android is increasing in popularity, but the open development platform that developers love is also attractive to malware creators. Download this white paper to learn more about secure code signing practices that can help keep Android apps secure. http://pubads.g.doubleclick.net/gampad/clk?id=65839951&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
