There have been numerous approaches for the optimal selection of wavelet basis. Two well known approaches are the 'matching pursuit' and 'entropy based' algorithms. While these approaches have been shown to have good results, they suffer by having large, highly redundant dictionaries in order to represent complex waveforms. In this paper, we present a novel approach for selecting independent wavelet feature basis. In this approach we will leverage the neural net 'super mother' principal along with neural net blind demixing/deconvolution techniques based on the statistical mechanics canonical ensemble for constrained Max-Ent approach with selection of basis may be ideal for independent feature extraction in reducing processing requirement for invariant pattern recognition.

Date Published: 26 March 1998
PDF: 12 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304869

Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)