  In this paper, we propose a new max-margin based discriminative feature
learning method. Specifically, we aim at learning a low-dimensional feature
representation, so as to maximize the global margin of the data and make the
samples from the same class as close as possible. In order to enhance the
robustness to noise, a $l_{2,1}$ norm constraint is introduced to make the
transformation matrix in group sparsity. In addition, for multi-class
classification tasks, we further intend to learn and leverage the correlation
relationships among multiple class tasks for assisting in learning
discriminative features. The experimental results demonstrate the power of the
proposed method against the related state-of-the-art methods.
