An EM-Based Piecewise Linear Regression Algorithm

Authors:
Sebastian Nusser;Clemens Otte;Werner Hauptmann
Affiliations:
Siemens AG, Corporate Technology, Munich, Germany 81730 and School of Computer Science, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany 39106;Siemens AG, Corporate Technology, Munich, Germany 81730;Siemens AG, Corporate Technology, Munich, Germany 81730
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

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Abstract

This contribution describes an EM-like piecewise linear regression algorithm that uses information about the target variable to determine a meaningful partitioning of the input space. The main goal of this approach is to incorporate information about the target variable in the prototype selection process of a piecewise regression approach. Furthermore, the proposed approach is designed to provide an interpretable solution by restricting the dimensionality of the local regression models. We will show that our approach achieves a similar predictive performance on benchmark problems compared to standard regression methods --- while the model complexity of our approach is reduced.