Class-proximity SOM and its applications in classification

Abstract

In this study, we propose a model of self-organizing map (SOM) capable of mapping high dimensional data into a low dimension space by preserving not only the feature-proximity of the original data but also their class-proximity. A conventional SOM is known to map original high dimensional data with similar features into points located close to each other in the low dimensional map in a so called competitive layer. In addition to this feature, the proposed SOM is also able to map high dimensional data belonging to a same class in each other's proximities. These characteristics retains the ability of the map to be used as a visualization tool of high dimensional data while also support the execution of high quality pattern classifications in the low dimensional map. In the experiments the classification performance of the proposed SOM is compared to that of MLP with regards to wide varieties of problems.