Simplified theory of automatic feature extraction in a noniterative neural network pattern recognition system
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
Whenever the input training class patterns applied to a one- layered, hard-limited perceptron (OHP) satisfy a certain positive-linear-independence (PLI) condition, the learning of these patterns by the neural network can be done non- iteratively in a few algebraic steps and the recognition of the untrained test patterns can be very accurate and very robust if a special learning scheme - automatic feature extraction - is adopted in the learning mode. In this paper, we report the theoretical foundation, the simplified design analysis of this novel pattern recognition system, and the experiments we carried out with this novel system. The experimental result shows that the learning of four digitized training patterns is close to real-time, and the recognition of the untrained patterns is above 90 percent correct. The ultra-fast learning speed here is due to the non-iterative nature of the novel learning scheme we used in OHP. The high robustness in recognition here is due to the automatic feature extraction scheme we use in the learning mode.