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23 September 1999 Automatic feature extraction using N-dimension convexity concept in a novel neural network
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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); doi: 10.1117/12.364098; https://doi.org/10.1117/12.364098
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