TY - GEN
T1 - Pattern recognition system design based on LDPC matrices
AU - O'Sullivan, Joseph A.
AU - Lai, Po Hsiang
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/33749451196
U2 - 10.1109/ISIT.2005.1523287
DO - 10.1109/ISIT.2005.1523287
M3 - Conference contribution
AN - SCOPUS:33749451196
SN - 0780391519
SN - 9780780391512
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 33
EP - 36
BT - Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05
T2 - 2005 IEEE International Symposium on Information Theory, ISIT 05
Y2 - 4 September 2005 through 9 September 2005
ER -