a julia implementation of Random Projection Tree Classifier

The implementation follows the papers of Das Gupta and Freund.

The data are first stored in a root node of a tree. Then a binary tree is created by propagating data down to leaves according to random projection and a sphericity constrains inside nodes. We thus get a collection of $2^{d}$ leaves if d is the depth of the tree asked for.

Licensed under either of

• Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0

• MIT license http://opensource.org/licenses/MIT

at your option.

1. DasGupta S. Freund Y. Random projection trees for vector quantization 2009.
2. DasGupta S. Freund Y. Random projection trees and low dimensional manifolds 2007.