On 03/26/2012 12:06 AM, Gael Varoquaux wrote: > On Sun, Mar 25, 2012 at 11:56:31PM +0200, Andreas wrote: > >> As far as I can see, your groups are "KMeans + Ward" and "rest". >> I don't know how ward works but looking at the lena example, >> the clusters don't seem to be convex. >> > But you are looking in the wrong space: the physical space, and not the > feature space. > > Yeah, stupid me. I was wondering whether x, y is in the feature space at all (it is not in the example), and then I thought "well that doesn't look convex". Maybe I should go to bed ...
>> Skimming the tutorial, I am not sure what the answer to that question is. >> I think they are both relaxations of the normalized cuts problem, >> but lead to different solutions in gerneral. >> > Back when I looked at that in details, I convinced myself that they were > solving the same problem, but one was using a positive-definite eigen > value problem, and the other a general one. The positive-definite problem > is much easier to solve, and more stable numerically. > > Ok then I'll believe you now :) Also I have learned something about spectral clustering. Thanks! :) ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
