Editing Dimensionality reduction (section)

===Generalized discriminant analysis (GDA)===
GDA deals with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the [[support-vector machine]]s (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space.<ref name="gda">{{cite journal |doi=10.1162/089976600300014980 |pmid=11032039 |title=Generalized Discriminant Analysis Using a Kernel Approach |journal=Neural Computation |volume=12 |issue=10 |pages=2385–2404 |year=2000 |last1=Baudat |first1=G. |last2=Anouar |first2=F. |citeseerx=10.1.1.412.760 |s2cid=7036341}}</ref><ref name="cloudid">{{cite journal |doi=10.1016/j.eswa.2015.06.025 |title=CloudID: Trustworthy cloud-based and cross-enterprise biometric identification |journal=Expert Systems with Applications |volume=42 |issue=21 |pages=7905–7916 |year=2015 |last1=Haghighat |first1=Mohammad |last2=Zonouz |first2=Saman |last3=Abdel-Mottaleb |first3=Mohamed}}</ref> Similar to LDA, the objective of GDA is to find a projection for the features into a lower dimensional space by maximizing the ratio of between-class scatter to within-class scatter.