A note on self-organizing semantic maps

Abstract

This paper discusses Kohonen's self-organizing semantic map (SOSM). We show that augmentation and normalization of numerical feature data as recommended for the SOSM is entirely unnecessary to obtain semantic maps that exhibit semantic similarities between objects represented by the data. Visual displays of a small data set of 13 animals based on principal components, Sammon's algorithm, and Kohonen's (unsupervised) self-organizing feature map (SOFM) possess exactly the same qualitative information as the much more complicated SOSM display does.