Visualizing similarities in high dimensional input spaces with a growing and splitting neural network

Abstract

The recognition of similarities in high dimensional input spaces and their visualization in low dimensional output spaces is a highly demanding application area for unsupervised artificial neural networks. Some of the problems inherent to structuring high dimensional input data may be shown with an application such as text document classification. One of the most prominent ways to represent text documents is by means of keywords extracted from the full-text of the documents and thus, document collections represent high dimensional input spaces by nature. We use a growing and splitting neural network as the underlying model for document classification. The apparent advantage of such a neural network is the adaptive network architecture that develops according to the specific requirements of the actual input space. As a consequence, the class structure of the input data becomes visible due to the separation of units into disconnected areas. The results from a growing and splitting neural network are further contrasted to the more conventional neural network approach to classification by means of self-organizing maps.