Higher-rank of SOM with observation-invariant distance mesure

Abstract

This study aims to develop an observation-invariant classification algorithm of multi-class models.The task of the architecture is to classify a set of datasets, each of which is transformed differently when it is observed.Therefore it is required to remove observation-dependent component from the datasets.The higher-rank of SOM (SOM^n or `SOM of SOMs') was employed for multi-datasets classification task, and an observation-invariant distance measured was introduced into the SOM^n algorithm.In this paper, we applied the algorithm to a classification task of a set of `skyline shapes'.