Approximating Majority Depth
Computational Geometry
2013-06-17 v3
Abstract
We consider the problem of approximating the majority depth (Liu and Singh, 1993) of a point q with respect to an n-point set, S, by random sampling. At the heart of this problem is a data structures question: How can we preprocess a set of n lines so that we can quickly test whether a randomly selected vertex in the arrangement of these lines is above or below the median level. We describe a Monte-Carlo data structure for this problem that can be constructed in O(nlog n) time, can answer queries O((log n)^{4/3}) expected time, and answers correctly with high probability.
Cite
@article{arxiv.1205.1524,
title = {Approximating Majority Depth},
author = {Dan Chen and Pat Morin},
journal= {arXiv preprint arXiv:1205.1524},
year = {2013}
}
Comments
9 pages; no figures