Automatic feature extraction using N-dimension convexity concept in a novel neural network

Chia-Lun John Hu

doi:10.1117/12.364098

23 September 1999 Automatic feature extraction using N-dimension convexity concept in a novel neural network

Chia-Lun John Hu

Proceedings Volume 3811, Vision Geometry VIII; (1999) https://doi.org/10.1117/12.364098
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

As we published in the last few years, when the pattern vectors used in the training of a novel neural network satisfy a generalized N-dimension convexity property (or the novel PLI condition we derived), the neural network can learn these patterns very fast in a NONITERATIVE manner. The recognition of any UNTRAINED patterns by using this learned neural network can then reach OPTIMUM ROBUSTNESS if an automatic feature extraction scheme derived from the N-dimension geometry is used in the recognition mode. The simplified physical picture of the high-robustness reached by this novel system is the automatic extraction of the most distinguished parts in all the M training pattern vectors in the N-space such that the volume of the M-dimension parallelepiped spanned by these parts of the vectors reaches a maximum.

Citation Download Citation

Chia-Lun John Hu "Automatic feature extraction using N-dimension convexity concept in a novel neural network", Proc. SPIE 3811, Vision Geometry VIII, (23 September 1999); https://doi.org/10.1117/12.364098

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