2013/3/21 Ricardo Corral C. <ricardocorr...@gmail.com>: > Ok, this is a brief description of what I'm interested in. > > Recently, I faced a problem of evaluating the quality of a method to > obtain features from protein structures. > I adopted the approach given in [1] to measure separability of my > classes independently of my capacity of make good predictions. > This is basically a hypothesis testing of whether or not the > distribution of classes over feature vectors is somewhat random. > This test is made over the construction of a Relative Neighbourhood > Graph, which is O(n^3), thus, so prohibitive for practical use. > There is an efficient method for constructing RNG on the plane > described in [2] O(n*log(n)), but O(n^2) for a higher d dimension (in > fact O(n^2*f(d)) with f(d) <= (2*sqrt(d) +2)^d...). > > Actually, I have the test implemented, and I'm refining a speedup of > RNG construction based on the Half-Space Proximal (HSP) graph. This is > O(n^2log(n)), and there is no dependence of dimension other than time > consumed in calculating distances. > > This is made by doing RNG test over edges in HSP (attached images for > clarify this). > > Could this be of interest for sklearn users? And if so, be considered for > GSoC?
This looks interesting but please include the missing references. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_d2d_mar _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
