GraphLasso seems really neat (and the associated CV object should prove very useful).
I had a look at the stock market example but I am a bit confused by the fact that the clustering, graph structure and 2d-embedding seemed to be learned independently although they are clearly related problems. I see that the (dense) correlation matrix is used as input to affinity propagation. Wouldn't it be better if we used the partial correlations learned by GraphLasso directly? This way, the cluster centers and the edge structure would be more related. Likewise, the graph structure and the clustering are not used at all for learning the embedding, which seems like a pity. Also, I wonder if once we have the graph structure, couldn't we get away with just using a graph drawing algorithm? Mathieu ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
