Hinging Hyperplane Models for Multiple Predicted Variables

Anca Maria Ivanescu, Philipp Kranen, Thomas Seidl

Abstract: Model-based learning for predicting continuous values involves building an explicit generalization of the training data. Simple linear regression and piecewise linear regression techniques are well suited for this task, because, unlike neural networks, they yield an interpretable model. The hinging hyperplane approach is a nonlinear learning technique which computes a continuous model. It consists of linear submodels over individual partitions in the regressor space. However, it is only designed for one predicted variable. In the case of r predicted variables the number of partitions grows quickly with r and the result is no longer being compact or interpretable.