An Improved Generalized Discriminant Analysis for Large-Scale Data Set

In order to overcome the computation and storage problem for large-scale data set, an efficient iterative method of generalized discriminant analysis is proposed. Because sample vectors cannot explicitly be denoted in kernel space, some mathematical tricks are firstly used to transform the kernel matrix. Then, the columns of transformed matrix are used for iterative algorithm to extract nonlinear discriminant vectors. The proposed method reduces space complexity from O(m²) to O(m) and its effectiveness is validated from experimental results.