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In this paper, we propose an efficient method for resolving the optimal discriminant vectors of generalized discriminant analysis (GDA) and point out the drawback of high computational complexity in the traditional class-incremental GDA [W. Zheng, "Class-Incremental Generalized Discriminant Analysis", Neural Computation 18, 979-1006 (2006)]. Because there is no need to compute the mean of...
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...
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