A remark on global positioning from local distances

Amit Singer

doi:10.1073/pnas.0709842104

A remark on global positioning from local distances

Amit Singer

Research output: Contribution to journal › Article › peer-review

73 Scopus citations

Abstract

Finding the global positioning of points in Euclidean space from a local or partial set of pairwise distances is a problem in geometry that emerges naturally in sensor networks and NMR spectroscopy of proteins. We observe that the eigenvectors of a certain sparse matrix exactly match the sought coordinates. This translates to a simple and efficient algorithm that is robust to noisy distance data.

Original language	English (US)
Pages (from-to)	9507-9511
Number of pages	5
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	105
Issue number	28
DOIs	https://doi.org/10.1073/pnas.0709842104
State	Published - Jul 15 2008
Externally published	Yes

All Science Journal Classification (ASJC) codes

General

Keywords

Distance geometry
Eigenvectors
Multidimensional scaling
Sensor networks

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10.1073/pnas.0709842104

Cite this

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abstract = "Finding the global positioning of points in Euclidean space from a local or partial set of pairwise distances is a problem in geometry that emerges naturally in sensor networks and NMR spectroscopy of proteins. We observe that the eigenvectors of a certain sparse matrix exactly match the sought coordinates. This translates to a simple and efficient algorithm that is robust to noisy distance data.",

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AB - Finding the global positioning of points in Euclidean space from a local or partial set of pairwise distances is a problem in geometry that emerges naturally in sensor networks and NMR spectroscopy of proteins. We observe that the eigenvectors of a certain sparse matrix exactly match the sought coordinates. This translates to a simple and efficient algorithm that is robust to noisy distance data.

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KW - Eigenvectors

KW - Multidimensional scaling

KW - Sensor networks

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A remark on global positioning from local distances

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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