License: 
Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SoCG.2019.57
URN: urn:nbn:de:0030-drops-104617
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10461/
Tao, Yufei ;
Wang, Yu
Distribution-Sensitive Bounds on Relative Approximations of Geometric Ranges
Abstract
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.
BibTeX - Entry
@InProceedings{tao_et_al:LIPIcs:2019:10461,
author = {Yufei Tao and Yu Wang},
title = {{Distribution-Sensitive Bounds on Relative Approximations of Geometric Ranges}},
booktitle = {35th International Symposium on Computational Geometry (SoCG 2019)},
pages = {57:1--57:14},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-104-7},
ISSN = {1868-8969},
year = {2019},
volume = {129},
editor = {Gill Barequet and Yusu Wang},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2019/10461},
URN = {urn:nbn:de:0030-drops-104617},
doi = {10.4230/LIPIcs.SoCG.2019.57},
annote = {Keywords: Relative Approximation, Disagreement Coefficient, Data Summary}
}
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Keywords: |
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Relative Approximation, Disagreement Coefficient, Data Summary |
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Collection: |
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35th International Symposium on Computational Geometry (SoCG 2019) |
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Issue Date: |
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2019 |
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Date of publication: |
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11.06.2019 |
