<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

Directional representation of data in Linear Discriminant Analysis

Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznań, Poland

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Directional representation of data in Linear Discriminant Analysis"}

Sometimes feature representations of measured individuals are better described by spherical coordinates than Cartesian ones. The author proposes to introduce a...

Directional representation of data in Linear Discriminant Analysis

Online veröffentlicht: 12. Dez. 2015

Seitenbereich: 55 - 74

DOI: https://doi.org/10.1515/bile-2015-0006

Schlüsselwörter
LDA, pattern recognition, spherical coordinates, dimension reduction, PCA, directional statistics

© by Jolanta Grala-Michalak

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Directional representation of data in Linear Discriminant Analysis

Online veröffentlicht: 12. Dez. 2015

Seitenbereich: 55 - 74

DOI: https://doi.org/10.1515/bile-2015-0006

SchlüsselwörterLDA, pattern recognition, spherical coordinates, dimension reduction, PCA, directional statistics

© by Jolanta Grala-Michalak

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
LDA, pattern recognition, spherical coordinates, dimension reduction, PCA, directional statistics