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A Novel Dimension Reduction Approach for Mixture Discriminant Analysis of the High-Dimensional Data
Murat Erisoglu, Ulku Erisoglu

Last modified: 2015-09-05

Abstract


In this study, we proposed a novel dimension reduction approach for mixture discriminant analysis on based mixture of multivariate normal distributions of high-dimensional data. We considered case of a classification problem that the number of observations (n) is less than the number of variables (p). The proposed approaches compared with classical dimension reduction methods such as F approach, principal component analysis, clustering of variables and multidimensional scaling.


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