Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.2 9/5/84; site turtlevax.UUCP Path: utzoo!watmath!clyde!burl!ulysses!unc!mcnc!decvax!decwrl!flairvax!turtlevax!ken From: ken@turtlevax.UUCP (Ken Turkowski) Newsgroups: net.math Subject: Re: curve fitting (maybe) Message-ID: <522@turtlevax.UUCP> Date: Sun, 16-Sep-84 14:56:33 EDT Article-I.D.: turtleva.522 Posted: Sun Sep 16 14:56:33 1984 Date-Received: Tue, 25-Sep-84 04:02:04 EDT References: <1197@cwruecmp.UUCP> Organization: CADLINC, Inc. @ Palo Alto, CA Lines: 23 > given: any number of points in n-space and any number of vectors (of course > also in n-space) associated with each point. > > find: for any given point, x, find its most likely vector. > Sounds like a quantization problem to me. There was a paper in maybe IEEE computer graphics and applications recently about scanning a sample space with Peano curves to get clusters of sample points. If your quantization values ("vectors") are fixed, then all you need is an appropriate metric to determine the distance of each "point" to each "vector". Then pick the closest "vector". I've been using quotes because of the nonstandard usage of the term "vector". A vector is a distance with a direction, and has no root; i.e. it can float around all over the place. I suspect that you really mean a vector rooted at the origin. That brings up the question as to why you didn't use the term "point" for both. -- Ken Turkowski @ CADLINC, Palo Alto, CA UUCP: {amd,decwrl,dual,flairvax,nsc}!turtlevax!ken ARPA: turtlevax!ken@DECWRL.ARPA