SC²S Colloquium - June 1, 2017
|Date:||June 1, 2017|
|Time:||16:00 am, s.t.|
Katrin Degel: Sparse grid refinement for multiple classes
With the help of sparse grids, high dimensional data sets are classified. The classification uses the grid points to approximate the density functions for the given classes. Based on the problem, grid points are added in areas of interest. Those are regions where classes overlap or have common boundaries. When doing a pairwise comparision of classes, the execution time increases quadratically with the number of classes. Thus, an adaptation to the zero-crossing based refinement is made to reduce the class dependend scaling. In this work, the sparse grid toolbox of SG++ is expanded by a refinement functor, created to optimize the classification task with multiple classes. The newly introduced datastructures and algorithms are presented and an overview over the parameters is given. An evaluation is provided to compare the implemented refinement functor with the existing refinement functors. The evaluation is based on a four-dimensional data set, containing around two million data points in three classes.