Towards Robust Discriminative Projections Learning via Non-greedy l 2,1 -Norm MinMax.

Linear Discriminant Analysis (LDA) is one of the most successful supervised dimensionality reduction methods and has been widely used in many real-world applications. However, $\ell _2$ℓ2-norm is employed as the distance metric in the objective of LDA, which is sensitive to outliers. Many previous works improve the robustness of LDA by using $\ell _1$ℓ1-norm distance. However, the robustness again...