On Sat, Nov 12, 2011 at 12:28 AM, Gael Varoquaux <[email protected]> wrote:
> I don't like the results as much. Basically, we can either cluster on > conditional relations, or on marginal relations: basically, as an input > of affinity propagation, we can use the correlation matrix, which is a > standard affinity measure, or the precision matrix, which is more like a > condition affinity. They will give different information. Both > information are interesting. That said, we can run the affinity > propagation on the covariance matrix estimated by the GraphLasso, it > gives the same result, but may be more clear in terms of linking the > model. If people think it gives more clear picture, I'll implement the > change. Yes, it may help understand how the three components fit together. In a sense, this visualization gives three sources of information (the node position found by locally linear embedding, the edge strengths found by GraphLasso, the node color found by affinity propagation) into a single picture, which I guess is nice :) Cheers, Mathieu 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
