Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Abstract
We examine the Bayes-consistency of a recently proposed 1-nearest-neighbor-based multiclass learning algorithm. This algorithm is derived from sample compression bounds and enjoys the statistical advantages of tight, fully empirical generalization bounds, as well as the algorithmic advantages of a faster runtime and memory savings. We prove that this algorithm is strongly Bayes-consistent in metric spaces with finite doubling dimension --- the first consistency result for an efficient nearest-neighbor sample compression scheme. Rather surprisingly, we discover that this algorithm continues to be Bayes-consistent even in a certain infinite-dimensional setting, in which the basic measure-theoretic conditions on which classic consistency proofs hinge are violated. This is all the more surprising, since it is known that -NN is not Bayes-consistent in this setting. We pose several challenging open problems for future research.
Cite
@article{arxiv.1705.08184,
title = {Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions},
author = {Aryeh Kontorovich and Sivan Sabato and Roi Weiss},
journal= {arXiv preprint arXiv:1705.08184},
year = {2019}
}