title: Max-Margin-Based Discriminative Feature Learning.
abstract: In this brief, we propose a new max-margin-based discriminative feature learning method. In particular, 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, we leverage a regularization term to make the transformation matrix sparse in rows. In addition, we further learn and leverage the correlations among multiple categories for assisting in learning discriminative features. The experimental results demonstrate the power of the proposed method against the related state-of-the-art methods.
authors: Changsheng
authors: C
authors: Li
authors: Qingshan
authors: Q
authors: Liu
authors: Weishan
authors: W
authors: Dong
authors: Fan
authors: F
authors: Wei
authors: Xin
authors: X
authors: Zhang
authors: Lin
authors: L
authors: Yang