Heteroscedastic Max-min Distance Analysis for Dimensionality Reduction

Max-min distance analysis (MMDA) performs dimensionality reduction by maximizing the minimum pairwise distance between classes in the latent subspace under the homoscedastic assumption, which can address the class separation problem caused by the Fisher criterion, but is incapable of tackling hetero...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - (2018) vom: 21. Mai
1. Verfasser: Su, Bing (VerfasserIn)
Weitere Verfasser: Ding, Xiaoqing, Liu, Changsong, Wu, Ying
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article