Geometric diffusions for the analysis of data from sensor networks

Ronald R. Coifman; Mauro Maggioni; Steven W. Zucker; Ioannis G. Kevrekidis

doi:10.1016/j.conb.2005.08.012

Geometric diffusions for the analysis of data from sensor networks

Ronald R. Coifman, Mauro Maggioni, Steven W. Zucker, Ioannis G. Kevrekidis

Chemical & Biological Engineering

Research output: Contribution to journal › Review article › peer-review

12 Scopus citations

Abstract

Harmonic analysis on manifolds and graphs has recently led to mathematical developments in the field of data analysis. The resulting new tools can be used to compress and analyze large and complex data sets, such as those derived from sensor networks or neuronal activity datasets, obtained in the laboratory or through computer modeling. The nature of the algorithms (based on diffusion maps and connectivity strengths on graphs) possesses a certain analogy with neural information processing, and has the potential to provide inspiration for modeling and understanding biological organization in perception and memory formation.

Original language	English (US)
Pages (from-to)	576-584
Number of pages	9
Journal	Current Opinion in Neurobiology
Volume	15
Issue number	5
DOIs	https://doi.org/10.1016/j.conb.2005.08.012
State	Published - Oct 2005

All Science Journal Classification (ASJC) codes

General Neuroscience

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10.1016/j.conb.2005.08.012

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title = "Geometric diffusions for the analysis of data from sensor networks",

abstract = "Harmonic analysis on manifolds and graphs has recently led to mathematical developments in the field of data analysis. The resulting new tools can be used to compress and analyze large and complex data sets, such as those derived from sensor networks or neuronal activity datasets, obtained in the laboratory or through computer modeling. The nature of the algorithms (based on diffusion maps and connectivity strengths on graphs) possesses a certain analogy with neural information processing, and has the potential to provide inspiration for modeling and understanding biological organization in perception and memory formation.",

author = "Coifman, {Ronald R.} and Mauro Maggioni and Zucker, {Steven W.} and Kevrekidis, {Ioannis G.}",

note = "Funding Information: The authors would like to thank Y Keller, S Lafon, AB Lee and B Nadler for providing some of the figures, and P Jones for useful comments during the preparation of the manuscript. Research partially supported by Defence Advanced Research Projects Agency. M Maggioni is partially supported by National Science Foundation grant DMS-0512050.",

year = "2005",

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doi = "10.1016/j.conb.2005.08.012",

language = "English (US)",

volume = "15",

pages = "576--584",

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T1 - Geometric diffusions for the analysis of data from sensor networks

AU - Coifman, Ronald R.

AU - Maggioni, Mauro

AU - Zucker, Steven W.

AU - Kevrekidis, Ioannis G.

N1 - Funding Information: The authors would like to thank Y Keller, S Lafon, AB Lee and B Nadler for providing some of the figures, and P Jones for useful comments during the preparation of the manuscript. Research partially supported by Defence Advanced Research Projects Agency. M Maggioni is partially supported by National Science Foundation grant DMS-0512050.

PY - 2005/10

Y1 - 2005/10

N2 - Harmonic analysis on manifolds and graphs has recently led to mathematical developments in the field of data analysis. The resulting new tools can be used to compress and analyze large and complex data sets, such as those derived from sensor networks or neuronal activity datasets, obtained in the laboratory or through computer modeling. The nature of the algorithms (based on diffusion maps and connectivity strengths on graphs) possesses a certain analogy with neural information processing, and has the potential to provide inspiration for modeling and understanding biological organization in perception and memory formation.

AB - Harmonic analysis on manifolds and graphs has recently led to mathematical developments in the field of data analysis. The resulting new tools can be used to compress and analyze large and complex data sets, such as those derived from sensor networks or neuronal activity datasets, obtained in the laboratory or through computer modeling. The nature of the algorithms (based on diffusion maps and connectivity strengths on graphs) possesses a certain analogy with neural information processing, and has the potential to provide inspiration for modeling and understanding biological organization in perception and memory formation.

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Geometric diffusions for the analysis of data from sensor networks

Abstract

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