Real-time classifier based on adaptive competitive self-organizing algorithm

Abstract

This article introduces a novel adaptive competitive self-organizing (ACS) model, with applicability for real-time clustering and vector quantization. An important feature of this model is its dynamic structure and self-adjusting parameters that also offers a solution to the problem of parasitic limit points and consequently in more accurate label assignments. This unsupervised classifier is free of any external control mechanism. Its self-organizing (SO) dynamic is governed by the gradient descent (GD) theory in cooperation with a competition mechanism based on Lotka–Volterra competitive exclusion. The core algorithm of this classifier is based on developing an energy function, where its minima or equilibrium points correspond to the centroid of similar input patterns. Since this energy function is a form of Lyapunov function, it guarantees stabilization of the dynamical trajectories of labels in finite numbers of isolated equilibrium points. This energy function along with other control parameter functions, then, will be the base for the set of ordinary differential equations (ODEs) describing the overall dynamic of our system. Finally, the effectiveness of the proposed ACS model is demonstrated by implementing it on both real and artificial data sets as well as comparing with other well-known clustering methods. ACS method showed a better clustering performance in some categories and an overall comparable rendition.