Max-min distance analysis by using sequential SDP relaxation for dimension reduction
We propose a new criterion for discriminative dimension reduction, max-min distance analysis (MMDA). Given a data set with C classes, represented by homoscedastic Gaussians, MMDA maximizes the minimum pairwise distance of these C classes in the selected low-dimensional subspace. Thus, unlike Fisher&...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1998. - 33(2011), 5 vom: 25. Mai, Seite 1037-50 |
|---|---|
| Auteur principal: | |
| Autres auteurs: | |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2011
|
| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |