Skip to Main Content
Dual-space linear discriminant analysis (DSLDA) is a popular method for discriminant analysis. The basic idea of the DSLDA method is to divide the whole data space into two complementary subspaces, i.e., the range space of the within-class scatter matrix and its complementary space, and then solve the discriminant vectors in each subspace. Hence, the DSLDA method can take full advantage of the discriminant information of the training samples. However, from the computational point of view, the original DSLDA method may not be suitable for online training problems because of its heavy computational cost. To this end, we modify the original DSLDA method and then propose a data order independent incremental algorithm to accurately update the discriminant vectors of the DSLDA method when new samples are inserted into the training data set. We conduct experiments on the AR face database to confirm the better performance of the proposed algorithms in terms of the recognition accuracy and computational efficiency.