ABSTRACT Classification problems are commonly seen in practice. In this article, we aim to develop classifiers that can enjoy great interpretability as linear classifiers, and at the same time have model flexibility as nonlinear classifiers. We propose convex bidirectional large margin classifiers to fill the gap between linear and general nonlinear classifiers for high-dimensional data. Our method provides a new data visualization tool for classification of high-dimensional data. The obtained bilinear projection structure makes the proposed classifier very interpretable. Additional shrinkage to approximate variable selection is also considered. Through analysis of simulated and real data in high-dimensional settings, our method is shown to have superior prediction performance and interpretability when there are potential subpopulations in the data. The computer code of the proposed method is available as supplemental materials.

中文翻译：

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凸双向大余量分类器


摘要 分类问题在实践中很常见。在本文中，我们的目标是开发能够像线性分类器一样具有良好可解释性的分类器，同时具有像非线性分类器一样的模型灵活性。我们提出凸双向大边缘分类器来填补高维数据的线性和一般非线性分类器之间的差距。我们的方法为高维数据的分类提供了一种新的数据可视化工具。获得的双线性投影结构使得所提出的分类器非常可解释。还考虑了近似变量选择的额外收缩。通过对高维设置中的模拟数据和真实数据的分析，当数据中存在潜在的子群体时，我们的方法被证明具有优异的预测性能和可解释性。所提出方法的计算机代码可作为补充材料提供。