Biometrical Letters Vol. 52(2), 2015, pp. 55-74
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DIRECTIONAL REPRESENTATION OF DATA IN LINEAR DISCRIMINANT ANALYSIS Jolanta Grala-Michalak Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznań, Poland, grala@amu.edu.pl |
Sometimes feature representations of measured individuals are better described by spherical coordinates than Cartesian ones. The author proposes to introduce a pre-processing step in LDA based on the arctangent transformation of spherical coordinates. This nonlinear transformation does not change the dimension of the data, but in combination with LDA it leads to a dimension reduction if the raw data are not linearly separated. The method is presented using various examples of real and artificial data.
LDA, pattern recognition, spherical coordinates, dimension reduction, PCA, directional statistics
