I don't think this would work out-of-the-box. The classic ball tree implementation depends on the metric satisfying the triangle inequality. You may be able to cleverly modify the algorithm to work in other cases, but I'm not aware of any examples of that. I think that approximate nearest neighbor methods would be a better bet. Jake
Andreas wrote: > Hi everybody. > While reviewing the label propagation PR, I thought about the pairwise > rbf functions. > Would it be possible to compute an sparse, approximate RBF kernel matrix > using ball trees? > The idea would be that if the distance between two points is some > "large" multiple of gamma, the kernel can be assumed > to be zero. > Do you think this is feasible to implement and helpful for real data? > > Cheers, > Andy > > ------------------------------------------------------------------------------ > Try before you buy = See our experts in action! > The most comprehensive online learning library for Microsoft developers > is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, > Metro Style Apps, more. Free future releases when you subscribe now! > http://p.sf.net/sfu/learndevnow-dev2 > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
