On Tue, 02 Sep 2003 16:40:29 +0200, Cristian-Augustin Saita <[EMAIL PROTECTED]> wrote:
> 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. You can draw from a collection of the original points. Or you can use the original points but make them blurry. Or you can derive parameters from the original points and 'randomize' based on the parameters. Once you have said "No" those three things, alone and in every combination, I can't think of anything to suggest, except that you need to reconsider what you are trying to achieve. > > Thank you for your answers and for any references to similar solved or > unsolved problems, -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
