ABSTRACT Max-margin-based discriminative feature learning method (MMLDF) learns a low-dimensional feature representation such that the global margin of the data is maximised and samples of the same class become as close as possible. Non-linear version of MMLDF uses kernel method. In this paper, we first prove that the kernel MMLDF cannot be used for discriminative feature learning. Strictly speaking, the kernel MMLDF maps input vectors into zero vectors which cannot then be classified. Therefore, to overcome this problem, we propose a novel non-linear MMLDF which uses piece-wise learning technique. Then, by using alternating optimisation, an algorithm is proposed to solve our proposed model. Experimental results on real datasets confirm that our proposed piece-wise MMLDF outperforms linear MMLDF and piece-wise SVM.

中文翻译：

---

基于分段最大边距的判别特征学习

摘要 基于最大边距的判别特征学习方法 (MMLDF) 学习低维特征表示，从而使数据的全局边距最大化，并且同一类的样本尽可能接近。MMLDF 的非线性版本使用核方法。在本文中，我们首先证明核 MMLDF 不能用于判别特征学习。严格来说，内核 MMLDF 将输入向量映射到无法分类的零向量。因此，为了克服这个问题，我们提出了一种使用分段学习技术的新型非线性 MMLDF。然后，通过使用交替优化，提出了一种算法来解决我们提出的模型。在真实数据集上的实验结果证实，我们提出的分段 MMLDF 优于线性 MMLDF 和分段 SVM。