Approximating Higher-Order Distances Using Random Projections
Machine Learning
2012-03-19 v1 Machine Learning
Abstract
We provide a simple method and relevant theoretical analysis for efficiently estimating higher-order lp distances. While the analysis mainly focuses on l4, our methodology extends naturally to p = 6,8,10..., (i.e., when p is even). Distance-based methods are popular in machine learning. In large-scale applications, storing, computing, and retrieving the distances can be both space and time prohibitive. Efficient algorithms exist for estimating lp distances if 0 < p <= 2. The task for p > 2 is known to be difficult. Our work partially fills this gap.
Cite
@article{arxiv.1203.3492,
title = {Approximating Higher-Order Distances Using Random Projections},
author = {Ping Li and Michael W. Mahoney and Yiyuan She},
journal= {arXiv preprint arXiv:1203.3492},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)