Generalized Discriminant／Discriminative Analysis (GDA)

Generalized Discriminant/Discriminative Analysis (GDA)

• deals with nonlinear discriminant analysis using kernel function operator
• the underlying theory is close to the Support Vector Machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space
• similar to Linear Discriminant Analysis (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