Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms

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. 
DOI: 10.1007/s13218-017-0519-3

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Cite this paper

@article{Munteanu2017CoresetsMethodsAH, title={Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms}, author={Alexander Munteanu and Chris Schwiegelshohn}, journal={KI - K{\"{u}nstliche Intelligenz}, year={2017}, pages={1-17} }