Hello, Someone could tell me, please, if there exists any standard method of generating points in a multi-dimensional real hyper-space (like [0,1]^N, where N represents the number of dimensions) so to preserve the unknown space distribution of a given sample of points from the considered space.
More precisely, based on a sample of 60,000 mutidimensional points (representing colour characteristics of images from an image database), is it possible to generate a set of 1,000,000 points with garanties (of any nature) that the generated set preserves the (unknown) space distribution of the original sample set? I want to avoid to simply replicate/duplicate the original points, method which I'm not very sure that preserves the original distribution either. Thank you for your answers and for any references to similar solved or unsolved problems, Cristian . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
