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Label Propagation for Semi-Supervised Learning in Self-Organizing Maps

Herrmann, Lutz ; Ultsch, Alfred

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Semi-supervised learning aims at discovering spatial structures in high-dimensional input spaces when insufficient background information about clusters is available. A particulary interesting approach is based on propagation of class labels through proximity graphs. The Self-Organizing Map itself can be seen as such a proximity graph that is suitable for label propagation. It turns out that Zhu's popular label propagation method can be regarded as a modification of the SOM's well known batch learning rule. In this paper, an approach for semi-supervised learning is presented. It is based on label propagation in trained Self-Organizing Maps. Furthermore, a simple yet powerful method for crucial parameter estimation is presented. The resulting clustering algorithm is tested on the fundamental clustering problem suite (FCPS).

Keywords: semi-supervised learning, label propagation, clustering, visualization
Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Herrmann, Lutz ; Ultsch, Alfred  (2007)  Label Propagation for Semi-Supervised Learning in Self-Organizing Maps.

URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/141

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 Latest update: 15 Feb 2011
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