On Sun, Mar 25, 2012 at 11:38:50PM +0200, Andreas wrote: > > Unlike something like spectral clustering, it is the euclidean distance > > to the centers that is minimized. Thus K-Means will seek clusters that > > are regular in the flat euclidean space.
> Ok, that's right. Though I would argue that the distance measure > is not the only factor here. MeanShift also works with a euclidean > distance by using a Gaussian weighted density. > Here the difference is more in the objective then in the underlying > distance. Granted. Do you have suggestions for a better formulation? It would be helpful. > I'm tempted but would like to avoid going down that road ;) > >> Basically Shi/Malik multipy by the inverse diagonal from the right while > >> Jordan/Ng multiply by the square root form left and right - or something ;) > > 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? 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
