Geometries of sensor outputs, inference and information processing

Ronald R. Coifman, Stephane Lafon, Mauro Maggioni, Yosi Keller, Arthur D. Szlam, Frederick J. Warner, Steven W. Zucker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

We describe signal processing tools to extract structure and information from arbitrary digital data sets. In particular heterogeneous multi-sensor measurements which involve corrupt data, either noisy or with missing entries present formidable challenges. We sketch methodologies for using the network of inferences and similarities between the data points to create robust nonlinear estimators for missing or noisy entries. These methods enable coherent fusion of data from a multiplicity of sources, generalizing signal processing to a non linear setting. Since they provide empirical data models they could also potentially extend analog to digital conversion schemes like "sigma delta".

Original languageEnglish
Title of host publicationIntelligent Integrated Microsystems
DOIs
StatePublished - 2006
Externally publishedYes
EventIntelligent Integrated Microsystems - Kissimmee, FL, United States
Duration: 19 Apr 200621 Apr 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6232
ISSN (Print)0277-786X

Conference

ConferenceIntelligent Integrated Microsystems
Country/TerritoryUnited States
CityKissimmee, FL
Period19/04/0621/04/06

Keywords

  • Diffusion on manifolds
  • Laplace-Beltrami operator
  • Markov processes
  • Multiscale analysis

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