Topological Data Mapping for Improved Generalization Capabilities using Counter Propagation Networks

This paper presents a method to improve generalization capabilities of supervised neural networks based on topological data mapping used in Counter Propagation Networks (CPNs). Our method provides advantages to interpolate new data in sparse areas that exist among categories and to remove overlapping or conflicting data in original training data. Moreover, our method can control the number of training data by changing the size of the category map according to a problem to be solved. We applied our method to classification problems of two-dimensional datasets for evaluation of basic characteristics of our method. The classification results show that decision boundaries are changed and that generalization capabilities are improved using our method. Moreover, we applied our method to face recognition under various illumination conditions using the Yale Face Database B. The results indicate that our method provides not only improved generalization capabilities, but also visualizes spatial distributions of support vectors on a category map.