Max-Margin based Discriminative Feature Learning
This work addresses feature learning for classification tasks, offering an incremental improvement with robustness enhancements.
The paper tackles the problem of learning discriminative features for classification by proposing a max-margin method that maximizes global data margin and minimizes intra-class distances, with results showing it outperforms state-of-the-art methods.
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.