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. > >>> But if you do it right, the two approaches solve the same problem, AFAIK. > >> They lead to different generalized eigenvalue problems: > >> http://arxiv.org/pdf/0711.0189 > > Yes (that's exactly the reference that I had in mind), but they should > > minimize the same energy, right? > 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. > The tutorial suggests using Shi/Malik but since both are in > use in the literature, I thought it would be nice to have them both. My understanding is that the non-positive-definite formulation really shouldn't be used, as it is slower and less stable. G ------------------------------------------------------------------------------ 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
