Topographic Mapping of Large Dissimilarity Data Sets

@article{Hammer2010TopographicMO,
  title={Topographic Mapping of Large Dissimilarity Data Sets},
  author={Barbara Hammer and Alexander Hasenfuss},
  journal={Neural Computation},
  year={2010},
  volume={22},
  pages={2229-2284}
}
Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectorial data sets; they require a classical feature encoding of information. Often data are available in the form of pairwise distances only, such as arise from a kernel matrix, a graph… CONTINUE READING
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