Integrated feature extraction using adaptive wavelets

On-line quality control in the manufacturing and processing industry is increasingly being undertaken by analyzing collinear data such as spectra sampled by a spectrometer. Spectral data are very highly dimensional and are characterized by having highly correlated, localized structures. Wavelets are therefore most effective in extracting the important local features in spectra by reducing the number of variables whilst, at the same time, retaining as much information as possible and facilitating the automated analysis and interpretation of spectra. There are many kinds of wavelets which exist in the literature, but the fundamental problem to overcome is deciding which wavelet will produce the best results for a particular application. Rather than using an 'off-the-shelf' wavelet, an automated search is performed for the wavelet which optimizes specified multivariate modeling criteria. The spectral data analyzed in this chapter are of importance to the agricultural, pharmaceutical and mining industries as well as the environmental sciences.

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Feature Extraction, Construction and Selection: a data mining perspective

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978-0-7923-8196-9

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