A Generic Sensor Fusion Problem: Classification and Function Estimation

TL;DR: A generic fusion problem is studied for multiple sensors whose outputs are probabilistically related to their inputs according to unknown distributions, and an empirical risk minimization method is described for designing fusers with distribution-free performance bounds.

Abstract: A generic fusion problem is studied for multiple sensors whose outputs are probabilistically related to their inputs according to unknown distributions. Sensor measurements are provided as iid input-output samples, and an empirical risk minimization method is described for designing fusers with distribution-free performance bounds. The special cases of isolation and projective fusers for classifiers and function estimators, respectively, are described in terms of performance bounds. The isolation fusers for classifiers are probabilistically guaranteed to perform at least as good as the best classifier. The projective fusers for function estimators are probabilistically guaranteed to perform at least as good as the best subset of estimators.