2011/11/11 Mathieu Blondel <[email protected]>: > 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.
Indeed I thought the same, but as the current result is already good / interesting ... > Also, I wonder if once we have > the graph structure, couldn't we get away with just using a graph > drawing algorithm? Yes but that requires an external dependency on something like dot or neato from GraphViz. It's better not to have such a dependency for building the documentation. But we could ad a note and maybe a helper python function to generate a ".dot" file from an adjacency matrix + node labels. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ 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
