Self–organization with partial data

Abstract

We show how the kohonen self-organizing feature map model can be extended so that partial training data can be utilized. Given input stimuli in which values for some elements or features are absent, the match computation and the weight updates are performed in the input subspace defined by the available values. Three examples, including an application to student modelling for intelligent tutoring systems in which data is inherently incomplete, demonstrate the effectiveness of the extension.