A Randomized Approximation Convex Hull Algorithm for High Dimensions

The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models that covers the whole input ranges in which they will be employed. The convex hull algorithm is applied as a method for data selection; however the use of conventional implementations of this method in high dimensions, due to its high complexity, is not feasible. In this paper, we propose a randomized approximation convex hull algorithm which can be used for high dimensions in an acceptable execution time.