Dimensionality Reduction using Generalized Discriminant Analysis (GDA)
Updated 08 Apr 2016
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.
(C) Mohammad Haghighat, University of Miami
PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.
Mohammad Haghighat (2021). Dimensionality Reduction using Generalized Discriminant Analysis (GDA) (https://github.com/mhaghighat/gda), GitHub. Retrieved .
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