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&...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 33(2011), 5 vom: 01. Mai, Seite 1037-50 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2011
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Online verfügbar |
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