文章基本信息

标题：Distribution-Sensitive Bounds on Relative Approximations of Geometric Ranges
本地全文：下载
作者：Yufei Tao ; Yu Wang
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2019
卷号：129
页码：1-14
DOI：10.4230/LIPIcs.SoCG.2019.57
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：A family R of ranges and a set X of points, all in R^d, together define a range space (X, R _X), where R _X = {X cap h h in R}. We want to find a structure to estimate the quantity X cap h / X for any range h in R with the (rho, epsilon)-guarantee: (i) if X cap h / X > rho, the estimate must have a relative error epsilon; (ii) otherwise, the estimate must have an absolute error rho epsilon. The objective is to minimize the size of the structure. Currently, the dominant solution is to compute a relative (rho, epsilon)-approximation, which is a subset of X with O~(lambda/(rho epsilon^2)) points, where lambda is the VC-dimension of (X, R _X), and O~ hides polylog factors. This paper shows a more general bound sensitive to the content of X. We give a structure that stores O(log (1/rho)) integers plus O~(theta * (lambda/epsilon^2)) points of X, where theta - called the disagreement coefficient - measures how much the ranges differ from each other in their intersections with X. The value of theta is between 1 and 1/rho, such that our space bound is never worse than that of relative (rho, epsilon)-approximations, but we improve the latter's 1/rho term whenever theta = o(1/(rho log (1/rho))). We also prove that, in the worst case, summaries with the (rho, 1/2)-guarantee must consume Omega(theta) words even for d = 2 and lambda <=3. We then constrain R to be the set of halfspaces in R^d for a constant d, and prove the existence of structures with o(1/(rho epsilon^2)) size offering (rho,epsilon)-guarantees, when X is generated from various stochastic distributions. This is the first formal justification on why the term 1/rho is not compulsory for "realistic" inputs.
关键词：Relative Approximation; Disagreement Coefficient; Data Summary