Sometimes feature representations of measured individuals are better described by spherical coordinates than Cartesian ones. The author proposes to introduce a preprocessing 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.
@article{bwmeta1.element.doi-10_1515_bile-2015-0006,
author = {Jolanta Grala-Michalak},
title = {Directional representation of data in Linear Discriminant Analysis},
journal = {Biometrical Letters},
volume = {52},
year = {2015},
pages = {55-74},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_1515_bile-2015-0006}
}
Jolanta Grala-Michalak. Directional representation of data in Linear Discriminant Analysis. Biometrical Letters, Tome 52 (2015) pp. 55-74. http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_1515_bile-2015-0006/
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