Nonlinear feature extraction using standard generalized discriminant analysis(GDA) has high computational
complexity in large datasets. Therefore, a greedy GDA(GGDA) is proposed to reduce training data and deal with the nonlinear
feature extraction problem. Firstly, a subset is selected from the full training data by using the greedy technique of the greedy
KPCA(GKPCA) method. Then, the feature extraction model is trained by using the GDA method with the subset instead of
the full training data. Finally, classification experiments using data of several feature extraction methods are performed. The
simulation results show that the feature extraction performance of both the GGDA and the GDA methods outperform that of
other methods. In addition of retaining the performance of the GDA method, the GGDA method reduces the computational
complexity of the nonlinear feature extraction in large datasets.