Branching competitive learning network: a novel self-creating model.

This paper presents a new self-creating model of a neural network in which a branching mechanism is incorporated with competitive learning. Unlike other self-creating models, the proposed scheme, called branching competitive learning (BCL), adopts a special node-splitting criterion, which is based mainly on the geometrical measurements of the movement of the synaptic vectors in the weight space. Compared with other self-creating and nonself-creating competitive learning models, the BCL network is more efficient to capture the spatial distribution of the input data and, therefore, tends to give better clustering or quantization results. We demonstrate the ability of the BCL model to appropriately estimate the cluster number in a data distribution, show its adaptability to nonstationary data inputs and, moreover, present a scheme leading to a multiresolution data clustering. Extensive experiments on vector quantization of image compression are given to illustrate the effectiveness of the BCL algorithm.