Growing Cell Structures - a Self-Organizing Network for Unsupervised and Supervised Learning

We present a new self-organizing neural network model having two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process which also includes occasional removal of units. The second variant of the model is a supervised learning method which results from the combination of the above mentioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible - in contrast to earlier approaches - to perform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks which generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented which are better than any results previously published.

ICSI Technical Report TR-93-026

Technical Report