DagSemProc.07161.2.pdf
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A general framework for unsupervised preocessing of structured data
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Dagstuhl Seminar Proceedings, Volume 7161
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2008-03-06
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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)
https://doi.org/10.4230/DagSemProc.07161.2
Abstract
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.
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Keywords
- Relational clustering
- median clustering
- recursive SOM models
- kernel SOM
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