A hierarchical ART network for the stable incremental learning of topological structures and associations from noisy data

In this article, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology-learning neural networks is presented. It enables stable on-line clustering of stationary and non-stationary input data by learning their inherent topology. Here, two network components representing two different levels of detail are trained simultaneously. By virtue of several filtering mechanisms, the sensitivity to noise is diminished, which renders the proposed network suitable for the application to real-world problems. Furthermore, we demonstrate that this network constitutes an excellent basis to learn and recall associations between real-world associative keys. Its incremental nature ensures that the capacity of the corresponding associative memory fits the amount of knowledge to be learnt. Moreover, the formed clusters efficiently represent the relations between the keys, even if noisy data is used for training. In addition, we present an iterative recall mechanism to retrieve stored information based on one of the associative keys used for training. As different levels of detail are learnt, the recall can be performed with different degrees of accuracy.

► An ART neural network learning topological structures is presented.
► It enables stable on-line clustering of stationary and non-stationary data.
► Noise-insensitive representations are learnt at two levels of detail.
► The network is extended to a hetero-associative memory for real-world keys.