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Munteanu/Schwiegelshohn/2018a: Coresets - Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms

Bibtype Article
Bibkey Munteanu/Schwiegelshohn/2018a
Author Munteanu, Alexander and Schwiegelshohn, Chris
Title Coresets - Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms
Journal KI - Künstliche Intelligenz
Volume 32
Number 1
Pages 37-53
Abstract We present a technical survey on the state of the art approaches in data reduction and the coreset framework. These include geometric decompositions, gradient methods, random sampling, sketching and random projections. We further outline their importance for the design of streaming algorithms and give a brief overview on lower bounding techniques.
Note KI special issue on 'Algorithmic Challenges and Opportunities of Big Data'
Year 2018
Projekt SFB876-A2, SFB876-C4
Url https://doi.org/10.1007/s13218-017-0519-3



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