Set constraint discovery: missing sensor data restoration using autoassociative regression machines

A sensor array can generate interdependent readings among the sensors. If the dependence is sufficiently strong, the readings may contain redundancy to the degree that the readings from one or more lost sensors may be able to be accurately estimated from those remaining. An autoassociative regression machine can learn the data interrelationships through inspection of historical data. Once trained, the autoassociative machine can be used to restore one or more arbitrary lost sensors if the data dependency is sufficiently strong. Recovery techniques include alternating projection onto convex sets (POCS) and iterative search algorithms