TurSOM: A paradigm bridging Turing's unorganized machines and self-organizing maps demonstrating dual self-organization

Abstract

Self-organization is a widely used technique in unsupervised learning and data analysis, largely exemplified by k-means clustering, self-organizing maps (SOM) and adaptive resonance theory. In this paper we present a new algorithm: TurSOM, inspired by Turing's unorganized machines and Kohonen's SOM. Turing's unorganized machines are an early model of neural networks characterized by self-organizing connections, as opposed to self-organizing neurons in SOM. TurSOM introduces three new mechanisms to facilitate both neuron and connection self-organization. These mechanisms are: a connection learning rate, connection reorganization, and a neuron responsibility radius. TurSOM is implemented in a 1-dimensional network (i.e. chain of neurons) to exemplify the theoretical implications of these features. In this paper we demonstrate that TurSOM is superior to the classical SOM algorithm in several ways: (1) speed until convergence; (2) independent clusters; and (3) tangle-free networks.