Statistical mechanics demixing approach to selection of independent wavelet basis
Harold H Szu, Paul G. Cox, Charles Chia-chuen Hsu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
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.