A New Dimension Reduction Approach Based on Distance for Mixture Discriminant Analysis of the High-Dimensional Data
Journal Title: Scholars Journal of Physics, Mathematics and Statistics - Year 2017, Vol 4, Issue 4
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.
Authors and Affiliations
Ulku Erisoglu, Aydın Karakoca, Ahmet Pekgor, Murat Erisoglu
Keywords
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