Method for reducing dimensionality in ATR systems

A method for robustly selecting reduced dimension statistics for pattern recognition systems is described. A stochastic model for each target or object is assumed parameterized by a finite dimensional vector. Data and parameter vectors are assumed to be long. As the size of these vectors increases, the performance improves to a point and then degrades; this trend is called the peaking phenomenon. A new, more robust method for selecting reduced dimension approximations is presented. This method selects variables if a measure of the amount of information provided exceeds a given level. This method is applied to distributions in the exponential family, performance is compared to other methods, and an analytical expression for performance is asymptotically approximated. In all cases studied, performance is better than with other known methods.