A general framework for unsupervised preocessing of structured data

Authors Barbara Hammer, Alessio Micheli, Alessandro Sperduti

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Barbara Hammer
Alessio Micheli
Alessandro Sperduti

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Barbara Hammer, Alessio Micheli, and Alessandro Sperduti. A general framework for unsupervised preocessing of structured data. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)


We propose a general framework for unsupervised recurrent and recursive networks. This proposal covers various popular approaches like standard self organizing maps (SOM), temporal Kohonen maps, resursive SOM, and SOM for structured data. We define Hebbian learning within this general framework. We show how approaches based on an energy function, like neural gas, can be transferred to this abstract framework so that proposals for new learning algorithms emerge.
  • Relational clustering
  • median clustering
  • recursive SOM models
  • kernel SOM


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