Article

Local embeddings of metric spaces

Authors:

Ofer NeimanAuthors Info & Claims

STOC '07: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing

Pages 631 - 640

https://doi.org/10.1145/1250790.1250883

Published: 11 June 2007 Publication History

Abstract

In many application areas, complex data sets are often representedby some metric space and metric embedding is used to provide a more structured representation of the data. In many of these applications much greater emphasis is put on the preserving the local structure of the original space than on maintaining its complete structure. This is also the case in some networking applications where "small world" phenomena in communication patterns has been observed. Practical study of embedding has indeed involved with finding embeddings with this property. In this paper we initiate thestudy of local embeddings of metric spaces and provide embeddings with distortion depending solely on the local structureof the space.

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Kocher PHorn JFogh AGenkin DGruss DHaas WHamburg MLipp MMangard SPrescher TSchwarz MYarom Y(2020)Spectre attacksCommunications of the ACM10.1145/339974263:7(93-101)Online publication date: 18-Jun-2020
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Local embeddings of metric spaces
1. Mathematics of computing
  1. Discrete mathematics
2. Theory of computation

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cover image ACM Conferences

STOC '07: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing

June 2007

734 pages

ISBN:9781595936318

DOI:10.1145/1250790

General Chair:
David Johnson
AT&T Labs - Research
,
Program Chair:
Uriel Feige
Microsoft Research and Weizmann Institute

Copyright © 2007 ACM.

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Published: 11 June 2007

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metric embedding

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June 11 - 13, 2007

California, San Diego, USA

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Overall Acceptance Rate 1,469 of 4,586 submissions, 32%

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https://dl.acm.org/doi/10.1145/3399742
Taylor MVega LKhazraee MMagaki IDavidson SRichmond D(2020)ASIC cloudsCommunications of the ACM10.1145/339973463:7(103-109)Online publication date: 18-Jun-2020
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Vankadara Lvon Luxburg U(2018)Measures of distortion for machine learningProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327397(4891-4900)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327345.3327397
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