A simple algorithm for convex hull determination in high dimensions

Selecting suitable data for neural network training, out of a larger set, is an important task. For approximation problems, as the role of the model is a nonlinear interpolator, the training data should cover the whole range where the model must be used, i.e., the samples belonging to the convex hull of the data should belong to the training set. Convex hull is also widely applied in reducing training data for SVM classification. The determination of the samples in the convex-hull of a set of high dimensions, however, is a time-complex task. In this paper, a simple algorithm for this problem is proposed.