Josh, The initial plan is to keep it quite simple. No ball trees, no enhancements. Ball trees are likely to require each node to have in memory the ground truth - too high a memory requirement. The goal is a simple KNN that uses O(c) memory in a map stage that assigns the class. Not very interesting.
Daniel. On Sat, Mar 26, 2011 at 4:23 PM, Josh Patterson <[email protected]> wrote: > What kind of approach would you use? I've done one of these before > with a balltree which was effective. I'd be interested in working on > spatial trees in mahout. > > Josh > > On Saturday, March 26, 2011, Daniel McEnnis <[email protected]> wrote: >> Dear Mahout developers, >> >> While I'm learning the code, I thought I'd ask if there was any >> objection to me working on a KNN classifier module as my learning >> project. I should be able to make this at worst O(n) space over the >> training set and O(c) space over the input set using Map Reduce. Its >> something I'm quite familiar with and fills a gap in the classifier >> portfolio. >> >> Sincerely, >> >> Daniel McEnnis. >> > > -- > Twitter: @jpatanooga > Solution Architect @ Cloudera > hadoop: http://www.cloudera.com > blog: http://jpatterson.floe.tv >
