Classification using adaptive wavelets for feature extraction
Journal Publication ResearchOnline@JCUAbstract
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 favoured 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.
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Volume
19
ISBN/ISSN
1939-3539
Edition
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Issue
10
Pages Count
9
Location
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Publisher
IEEE
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Publisher Location
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Publish Date
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Date
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EISSN
N/A
DOI
10.1109/34.625106