A Pattern Classification Method using Kernel Adaptive-Subspace Self-Organizing Map

Abstract

Adaptive-Subspace Self-Organizing Map (ASSOM) is a variant of Self-Organizing Map, where each computational unit defines a linear subspace. The subspace in a unit is represented by a set of basis vectors. After training, these units result in a set of subspace detectors. In numerous cases, however, these are not enough to describe a class of patterns because of a linearity. In this letter, the ASSOM on the high-dimensional space with kernel method is proposed in order to achieve efficient classification. By using the kernel method, linear subspaces in the ASSOM can be extended to non-linear subspaces easily. This improves the representation of subspace. The effectiveness of the proposed method is verified by applying it to a well-known problem, or two spirals classification.