Name
Abstract | ||
|---|---|---|
Collinear data such as spectra and time-varying signals are very high-dimensional and are characterized by having highly correlated, context-dependent localized structures. Feature mining involves extracting the important local features whilst, at the same time, retaining as much information as possible and facilitating the automated analysis and interpretation of the data. We present a novel wavelet-based feature mining approach which extracts the optimal features for a particular application. An automated search is performed for the wavelet which optimizes specified multivariate modeling criteria. In this paper we consider mapping analysis as the multivariate model and show how wavelets are able to elucidate the underlying group structure in the data. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-64383-4_27 | PAKDD |
Keywords | Field | DocType |
collinearity,adaptive wavelets,feature mining,mapping,collinear data,context dependent | Information theory,Data mining,Collinearity,Information processing,Computer science,Multivariate statistics,Feature mining,Multivariate analysis,Principal component analysis,Wavelet | Conference |
Volume | ISSN | ISBN |
1394 | 0302-9743 | 3-540-64383-4 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Y. de Vel | 1 | 21 | 5.01 |
| Danny Coomans | 2 | 105 | 19.07 |
| S. Patrick | 3 | 0 | 0.34 |