Name
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 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. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/34.625106 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
major concern,adaptive wavelet,standard wavelet base,manageable size,discriminant criterion,feature extraction,adaptive wavelets,available spectrum,discrete wavelet,innovative technique,discriminatory information,wavelet coefficient,frequency,optimization,dimensionality,wavelet analysis,design optimization,degradation,wavelet transforms,helium,discrete wavelet transform | Lifting scheme,Pattern recognition,Gabor wavelet,Computer science,Feature extraction,Discrete wavelet transform,Artificial intelligence,Cascade algorithm,Stationary wavelet transform,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
19 | 10 | 0162-8828 |
Citations | PageRank | References |
28 | 5.14 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yvette Mallet | 1 | 28 | 5.14 |
| Danny Coomans | 2 | 105 | 19.07 |
| Jerry Kautsky | 3 | 28 | 5.14 |
| Olivier Y. de Vel | 4 | 180 | 24.22 |