Manifold Classification by the SOM2: Application to the Face Image Classification

Abstract

Kohonen's self-organizing map (SOM) is an architecture that generates a map of a given dataset. In this paper, a novel extension of SOM called SOM2 is proposed. The mapping objects of SOM2 are SOMs themselves, each of which represents a set of data vectors. Thus, the entire SOM2 represents a set of data distributions. SOM2 is expected to be a powerful tool for the classification, estimation and recognition tasks relevant to nonlinear manifolds. In this paper, we applied SOM2 to the face image classification task.