Self-Organizing Maps for Time Series
Barbara Hammer, Alessio Micheli, Nicolas Neubauer, Alessandro Sperduti, Marc Strickert · 2005
We review a recent extension of the self-organizing map (SOM) for temporal structures with a simple recurrent dynamics leading to sparse representations, which allows an efficient training and a combination with arbitrary lattice structures. We discuss its practical applicability and its theoretical properties. Afterwards, we put the approach into a general framework of recurrent unsupervised models. This generic formulation also covers a variety of well-known alternative approaches including the temporal Kohonen map, the recursive SOM, and SOM for structured data. Based on this formulation, mathematical properties of the models are investigated. Interestingly, the dynamic can be generalized from sequences to more general tree structures thus opening the way to unsupervised processing of general data structures.