By minimizing the energy distance, the support points (SP) method can efficiently compact big training sample into a representative point set with small size. However, when the training sample is deficient, the quality of SP will be greatly reduced. In this paper, a sequential version of SP, called sequential support point (SSP), is proposed. The new method has two appealing features. First, the construction algorithm of SSP can adaptively update the proposal density in importance sampling process based on the existing information. Second, a hyperparameter is introduced to balance the representativeness of sequentially added points with the representativeness of overall points, so that some special purpose experimental designs, such as augmented design and sliced designs, can be efficiently constructed by setting the hyperparameter.

通过最小化能量距离，支持点（SP）方法可以有效地将大的训练样本压缩成一个小尺寸的代表点集。但是，当训练样本不足时，SP的质量会大大降低。在本文中，提出了一种顺序版本的 SP，称为顺序支持点 (SSP)。新方法有两个吸引人的特点。首先，SSP的构建算法可以根据现有信息自适应地更新重要性采样过程中的proposal密度。其次，引入超参数来平衡顺序添加点的代表性和整体点的代表性，从而通过设置超参数可以有效地构建一些特殊目的的实验设计，例如增强设计和切片设计。