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Online geometric reconstruction

Published: 20 July 2011 Publication History

Abstract

We investigate a new class of geometric problems based on the idea of online error correction. Suppose one is given access to a large geometric dataset though a query mechanism; for example, the dataset could be a terrain and a query might ask for the coordinates of a particular vertex or for the edges incident to it. Suppose, in addition, that the dataset satisfies some known structural property P (for example, monotonicity or convexity) but that, because of errors and noise, the queries occasionally provide answers that violate P. Can one design a filter that modifies the query's answers so that (i) the output satisfies P; (ii) the amount of data modification is minimized? We provide upper and lower bounds on the complexity of online reconstruction for convexity in 2D and 3D.

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  • (2020)Black-box methods for restoring monotonicityProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525262(3463-3473)Online publication date: 13-Jul-2020
  • (2018)Testing convexity of figures under the uniform distributionRandom Structures & Algorithms10.1002/rsa.2079754:3(413-443)Online publication date: 19-Sep-2018
  • (2017)Local Computation Algorithms for Graphs of Non-constant DegreesAlgorithmica10.1007/s00453-016-0126-y77:4(971-994)Online publication date: 1-Apr-2017
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  1. Online geometric reconstruction

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    Published In

    cover image Journal of the ACM
    Journal of the ACM  Volume 58, Issue 4
    July 2011
    145 pages
    ISSN:0004-5411
    EISSN:1557-735X
    DOI:10.1145/1989727
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 July 2011
    Accepted: 01 April 2011
    Revised: 01 December 2010
    Received: 01 July 2007
    Published in JACM Volume 58, Issue 4

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    Author Tags

    1. Computational geometry
    2. sublinear algorithms

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    Cited By

    View all
    • (2020)Black-box methods for restoring monotonicityProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525262(3463-3473)Online publication date: 13-Jul-2020
    • (2018)Testing convexity of figures under the uniform distributionRandom Structures & Algorithms10.1002/rsa.2079754:3(413-443)Online publication date: 19-Sep-2018
    • (2017)Local Computation Algorithms for Graphs of Non-constant DegreesAlgorithmica10.1007/s00453-016-0126-y77:4(971-994)Online publication date: 1-Apr-2017
    • (2016)Local ReconstructionEncyclopedia of Algorithms10.1007/978-1-4939-2864-4_698(1136-1139)Online publication date: 22-Apr-2016
    • (2015)Local ReconstructionEncyclopedia of Algorithms10.1007/978-3-642-27848-8_698-1(1-4)Online publication date: 30-Jun-2015
    • (2013)Testing and Reconstruction of Lipschitz Functions with Applications to Data PrivacySIAM Journal on Computing10.1137/11084074142:2(700-731)Online publication date: Jan-2013

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