DOI: 10.21276/sjpms.2017.4.4.9

Pages: 205-210

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 ( ) is less than the number of variables ( ). The proposed approaches compared with classical dimension reduction methods such as F approach, principal component analysis, clustering of variables and multidimensional scaling. Keywords: Dimension Reduction, High-dimensional data, Mixture discriminant analysis, F approach, Clustering of variables, SMACOF