Classification using adaptive wavelets for feature extraction

Mallet, Yvette, Coomans, Danny, Kautsky, Jerry, and De Vel, Olivier (1997) Classification using adaptive wavelets for feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (10). pp. 1058-1066.

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Abstract

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.

Item ID: 39729
Item Type: Article (Refereed Research - C1)
ISSN: 1939-3539
Funders: Australian Postgraduate Award
Date Deposited: 02 Sep 2015 03:37
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970103 Expanding Knowledge in the Chemical Sciences @ 100%
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