Andreas Rauber and Dieter Merkl
Department of Software Technology, Vienna University of Technology
Resselgasse 3 / 188, A-1040 Vienna, Austria.
e-mail: {rauber, merkl}@ifs.tuwien.ac.at
Self-organizing maps are a popular neural network model for mapping high-dimensional input data onto a lower-dimensional output space. However, as the size of the training data increases, both the necessary computational power as well as the training time required exceed tolerable limits. Still more important, not all training data may be available in one central location but may rather be collected and managed at different sites. This paper describes an approach for combining independent, distributed self-organizing maps to build a higher order map, allowing the creation and maintenance of scalable, independent map systems, which can be built to suit the individual needs of the users. This is achieved by training higher order maps using the trained lower order maps as input data. We demonstrate the applicability of this approach in the field of digital libraries.