Automatic feature extraction using a novel noniterative neural network
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
As we reported in the last few years, a one-layered, hard- limited perceptron is generally sufficient for carrying out a robust recognition on any untrained pattern if the training class patterns satisfy a certain PLI condition. For most pattern recognition applications, this condition should be satisfied. When this condition is satisfied, an automatic feature extraction scheme can then be derived using some N- dimension Euclidean geometry theories. This automatic scheme will automatically extract the most distinguished parts of the N-vectors used in the training. These distinguished parts or the feature vectors will then allow a very robust recognition when untrained patterns are tested in the recognition mode. Theoretical derivation and live experiments revealing the physical nature of this novel, ultra-fast learning, pattern recognition system will be presented in detail.