An incremental learning algorithm of Recursive Fisher Linear Discriminant

This paper presents an online feature extraction method called incremental recursive Fisher linear discriminant (IRFLD) whose batch learning algorithm called RFLD has been proposed by Xiang et al. In the conventional linear discriminant analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class scatter matrix. RFLD and the proposed IRFLD can eliminate this limitation. In the proposed IRFLD, the Pang et al.´s incremental linear discriminant analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional ILDA subspace. In addition, to estimate a suitable number of effective discriminant vectors, we also propose a convergence criterion for the recursive computations which is defined by using the class separability of discriminant features projected on the complementary subspace. The experimental results suggest that the recognition accuracies of IRFLD is improved as the learning proceeds. For several datasets, we confirm that the proposed IRFLD outperforms ILDA in terms of the recognition accuracy. However, the advantage of IRFLD against ILDA depends on datasets.