Hi, On Thu, Oct 21, 2010 at 12:12 PM, Martin Tomko <martin.to...@geo.uzh.ch> wrote: > Hi Steve, > tahnks for the hints and clarifications. > Unfortunately, I will not be able to use the approach you suggest, The > distances I generate are distances between VERY large matrices (say > 100000x100000 and more) each of different dimensions (not necessarily > square either), and there is no significance in terms of column properties, > they are basically graphs of sort. > > Is there a way out with the SVM, or I just forget that?
Well, it's not clear to me what type of data you are working with. You say they are "graphs of sort." There are "principled" ways of working with graphs in SVMs -- namely using "graph kernels". You can find information about them if you run through google (Karsten Borgwadt does a lot of work in this area). Unfortunately, I don't think there are any public-domain implementations out there for you to consume easily. But still -- you're able to calculate a distance metric over your data -- how are you doing that? Here's a shot at the dark, and probably not so correct, but read at your own risk: What if you try to create a kernel matrix by plugging your distance metric into the appropriate place from something like an RBF kernel function. For instance, the value of the RBF kernel between two points is: exp(-|X_1 - X_2|^2 / sigma^2) What if you plugged your distance measure between samples X_1 and X_2 into the |X_1 - X_2| slot and kept the rest the same? You have to verify that this is a valid kernel (gram) matrix -- I think it just needs to be symmetric positive definite. See a quick review here: http://www.support-vector.net/icml-tutorial.pdf Now your just left to figure out how to use ksvm (from kernlab) with kernel matrices and maybe you have something that can work. -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.