Directional representation of data in Linear Discriminant Analysis

Jolanta Grala-Michalak

doi:10.1515/bile-2015-0006

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Directional representation of data in Linear Discriminant Analysis

Biometrical Letters

Volume 52 (2015): Issue 2 (December 2015)

By: Jolanta Grala-Michalak

Open Access

|Dec 2015

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.

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Articles in this issue

DOI: https://doi.org/10.1515/bile-2015-0006 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811

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Language: English

Page range: 55 - 74

Published on: Dec 12, 2015

Published by: Polish Biometric Society

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

LDA,

pattern recognition,

spherical coordinates,

dimension reduction,

PCA,

directional statistics

Related subjects:

Life sciences,

Bioinformatics,

Life sciences, other,

Mathematics,

Probability and statistics,

Applied mathematics

© 2015 Jolanta Grala-Michalak, published by Polish Biometric Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 52 (2015): Issue 2 (December 2015)