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

Abstract

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).