Escaping the curse of dimensionality with a tree-based regressor
Machine Learning
2009-09-30 v1
Abstract
We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.
Cite
@article{arxiv.0902.3453,
title = {Escaping the curse of dimensionality with a tree-based regressor},
author = {Samory Kpotufe},
journal= {arXiv preprint arXiv:0902.3453},
year = {2009}
}