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Proceedings Paper

Geometries of sensor outputs, inference, and information processing
Author(s): Ronald R. Coifman; Stephane Lafon; Mauro Maggioni; Yosi Keller; Arthur D. Szlam; Frederick J. Warner; Steven W. Zucker
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Paper 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".

Paper Details

Date Published: 18 May 2006
PDF: 9 pages
Proc. SPIE 6232, Intelligent Integrated Microsystems, 623209 (18 May 2006); doi: 10.1117/12.669723
Show Author Affiliations
Ronald R. Coifman, Yale Univ. (United States)
Stephane Lafon, Google Inc. (United States)
Mauro Maggioni, Yale Univ. (United States)
Yosi Keller, Yale Univ. (United States)
Arthur D. Szlam, Yale Univ. (United States)
Frederick J. Warner, Yale Univ. (United States)
Steven W. Zucker, Yale Univ. (United States)

Published in SPIE Proceedings Vol. 6232:
Intelligent Integrated Microsystems
Ravindra A. Athale; John C. Zolper, Editor(s)

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