Abstract
We introduce relational variants of neural topographic maps including the self-organizing map and neural gas, which allow clustering and visualization of data given as pairwise similarities or dissimilarities with continuous prototype updates. It is assumed that the (dis-)similarity matrix originates from Euclidean distances, however, the underlying embedding of points is unknown. Batch optimization schemes for topographic map formations are formulated in terms of the given (dis-)similarities and convergence is guaranteed, thus providing a way to transfer batch optimization to relational data.
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Hasenfuss, A., Hammer, B. (2007). Relational Topographic Maps. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_9
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DOI: https://doi.org/10.1007/978-3-540-74825-0_9
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