Pattern recognition system design based on LDPC matrices

Pattern recognition systems may be designed to recognize an exponentially large number of objects from potentially noisy measurements. We propose a design based on storing compressed representations of binary patterns corresponding to objects of interest. Sensor measurements are similarly compressed and recognition proceeds by comparing the compressed sensor measurements to the compressed representations of the objects. Parity check matrices corresponding to low density parity check codes are used for the compression. This design yields an ensemble of systems such that the probability of error goes to zero as the length of the patterns grows