Geometries of sensor outputs, inference, and information processing
Ronald R. Coifman, Stéphane Lafon, Mauro Maggioni, Yosi Keller, Arthur D. Szlam, Frederick J. Warner, Steven W. Zucker · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
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".