CLASSIFICATION USING ADAPTIVE WAVELETS FOR FEATURE-EXTRACTION

A major concern arising from the classification of spectral data is th at the number of variables or dimensionality often exceeds the number of available spectra This leads to a substantial deterioration in perf ormance 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 t o reduce the dimensionality and optimize the discriminatory informatio n is presented. The discrete wavelet transform is utilized to produce wavelet coefficients which are used for classification. Rather than us ing one of the standard wavelet bases, we generate the wavelet which o ptimizes specified discriminant criteria.