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<p><b>Abstract</b>—A major concern arising from the classification of spectral data is that the number of variables or dimensionality often exceeds the number of available spectra. This leads to a substantial deterioration in performance of traditionally favored classifiers. It becomes necessary to decrease the number of variables to a manageable size, whilst, at the same time, retaining as much discriminatory information as possible. A new and innovative technique based on adaptive wavelets, which aims to reduce the dimensionality and optimize the discriminatory information is presented. The discrete wavelet transform is utilized to produce wavelet coefficients which are used for classification. Rather than using one of the standard wavelet bases, we generate the wavelet which optimizes specified discriminant criteria.</p>
r class category, s set of high-pass filter coefficients l, level of decomposed data or level of the discrete wavelet transform, t band at some level of the discrete wavelet transform, n number of cases, p dimensionality or number of variables.
Danny Coomans, Olivier De Vel, Yvette Mallet, Jerry Kautsky, "Classification Using Adaptive Wavelets for Feature Extraction", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1058-1066, October 1997, doi:10.1109/34.625106
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