Relational topographic maps

  • Authors:
  • Alexander Hasenfuss;Barbara Hammer

  • Affiliations:
  • Clausthal University of Technology, Department of Informatics, Clausthal-Zellerfeld, Germany;Clausthal University of Technology, Department of Informatics, Clausthal-Zellerfeld, Germany

  • Venue:
  • IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
  • Year:
  • 2007

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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.