On 1 Mar 2010 at 15:01, glen e. p. ropella wrote: > Thus spake Robert Holmes circa 10-03-01 02:49 PM: > > Once you get past about 10 dimensions you hit problems of "distance > > concentration" (as it's known in the machine learning community). Basically, > > all distances between pairs of points for D>10 are pretty much the same. > > That impacts any distance-based clustering or visualization techniques that > > you are trying to use. > > Is that true for all norms? Or just the standard 2-norm?
*Gotta* be possible to handpick a better norm in any given case, no? (But as you keep resampling and refining the data you might have to modify the choice of norm adaptively, which might defeat you in the end. It would help [me, at least] to have a more precise statement of what the reported phenomenon actually is. Robert? Should I just do a Google search for the conjoined phraes "distance concentration" and "machine learning", or can you speed up the process with a few more words?) ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
