Training data reduction and nonlinear feature extraction in classification based on greedy Generalized Discriminant Analysis

Generalized Discriminant Analysis (GDA) shows a powerful nonlinear feature extraction technique by kernel tricks. The size of its kernel matrix increases quadratically with the number of training data. For large training data set, it suffers from computational problem of diagonal and occupies large storage space of kernel matrix. Here, a more efficient nonlinear feature extraction method, Greedy Generalized Discriminant Analysis (GGDA) is presented to training data reduction and nonlinear feature extraction in classification. The simulation results indicate that the GGDA method reduces computational complexity due to the reduced training set in classification while retaining the performance of the GDA method.