Approximating Higher-Order Distances Using Random Projections
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 2 is known to be difficult. Our work partially fills this gap.
Cite
Text
Li et al. "Approximating Higher-Order Distances Using Random Projections." Conference on Uncertainty in Artificial Intelligence, 2010.Markdown
[Li et al. "Approximating Higher-Order Distances Using Random Projections." Conference on Uncertainty in Artificial Intelligence, 2010.](https://mlanthology.org/uai/2010/li2010uai-approximating/)BibTeX
@inproceedings{li2010uai-approximating,
title = {{Approximating Higher-Order Distances Using Random Projections}},
author = {Li, Ping and Mahoney, Michael W. and She, Yiyuan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2010},
pages = {312-321},
url = {https://mlanthology.org/uai/2010/li2010uai-approximating/}
}