Merge SOM for temporal data

The recent merging self-organizing map (MSOM) for unsupervised sequence processing constitutes a fast, intuitive, and powerful unsupervised learning model. In this paper, we investigate its theoretical and practical properties. Particular focus is put on the context established by the self-organizing MSOM, and theoretic results on the representation capabilities and the MSOM training dynamic are presented. For practical studies, the context model is combined with the neural gas vector quantizer to obtain merging neural gas (MNG) for temporal data. The suitability of MNG is demonstrated by experiments with artificial and real-world sequences with one- and multi-dimensional inputs from discrete and continuous domains.