[1/2] Hi guys, I've been hoping you could point me in the right direction (I suppose it's a bit of a "tool request"): I have a dataset of ~7k scattered points in 3D which represents a hypersurface that may or may not "fold unto itself". I know that I can detect if such folding occurs by examining the errors when performing leave-1-out cross-validation. I also know that CUDA-based algorithms exist to perform knn search. However, as I don't come from this field I can't tell if I can leverage a ...
... fast knn-search algorithm to speed up the leave-one-out error computation. Is there something "costly" I can compute once (kdtree?) and use it compute the error for all the points? The desired outcome in my case is a fast evaluation of the xval error (to compare how bad is the "folding" effect among hundreds of these hypersurfaces). Any other algorithm that could quantify the folding effect would also work. Thanks for any help! [2/2]
@Dev-iL, please ask that on the main site. There we have better facilities for asking & answering questions (e.g. formatting options will work) and the information will be available for people with the same question. That isn't a chat item.
