Dimensionality Reduction using Generalized Discriminant Analysis (GDA)
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 (2024). Dimensionality Reduction using Generalized Discriminant Analysis (GDA) (https://github.com/mhaghighat/gda), GitHub. Retrieved .
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Acknowledgements
Inspired by: Gabor Feature Extraction
Inspired: Gabor Wavelets, Feature fusion using Canonical Correlation Analysis (CCA), Feature fusion using Discriminant Correlation Analysis (DCA)
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