2014/1/28 Arnaud Joly <a.j...@ulg.ac.be>: > > The code looks very simple and we can reuse our sparse random > projection matrices to spare some memory and speed up projections. > > > I don’t know annoy, but could it be random projections trees > as in http://cseweb.ucsd.edu/~dasgupta/papers/rptree-stoc.pdf?
This is similar but instead of finding the RP direction based on a rule, Annoy chooses the direction at random but rejects the split if all the data at that node would end up on the same side of the hyperplane. It tries up to 20 times and do a random shuffling split instead if the RP attempts budget is consumed. Also the random projections trees does not really discuss applications to Approximate Nearest Neighbors search. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ WatchGuard Dimension instantly turns raw network data into actionable security intelligence. It gives you real-time visual feedback on key security issues and trends. Skip the complicated setup - simply import a virtual appliance and go from zero to informed in seconds. http://pubads.g.doubleclick.net/gampad/clk?id=123612991&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
