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18 May 2006 Geometries of sensor outputs, inference, and information processing
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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".
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Ronald R. Coifman, Stephane Lafon, Mauro Maggioni, Yosi Keller, Arthur D. Szlam, Frederick J. Warner, and Steven W. Zucker "Geometries of sensor outputs, inference, and information processing", Proc. SPIE 6232, Intelligent Integrated Microsystems, 623209 (18 May 2006);

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