Regularized linear discriminant analysis via a new difference-of-convex algorithm with extrapolation.

In this paper, we transform the classical linear discriminant analysis (LDA) into a smooth difference-of-convex optimization problem. Then, a new difference-of-convex algorithm with extrapolation is introduced and the convergence of the algorithm is established. Finally, for a face recognition problem, the proposed algorithm achieves better classification performance compared with several current algorithms in the literature. [ABSTRACT FROM AUTHOR]