Dimensionality Reduction using Generalized Discriminant Analysis (GDA)

Version 1.0.0.0 (5.05 KB) by Mohammad Haghighat
Generalized Discriminant Analysis - a non-linear feature dimensionality reduction technique
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Updated 8 Apr 2016
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GDA is one of dimensionality reduction techniques, which projects a data matrix from a high-dimensional space into a low-dimensional space by maximizing the ratio of between-class scatter to within-class scatter.

More details can be found in Section 4.3 of:

M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification," Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015.
http://dx.doi.org/10.1016/j.eswa.2015.06.025

(C) Mohammad Haghighat, University of Miami
haghighat@ieee.org
PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.

Cite As

Mohammad Haghighat (2026). Dimensionality Reduction using Generalized Discriminant Analysis (GDA) (https://github.com/mhaghighat/gda), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
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1.0.0.0

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