Sequential support points

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.