Robust Unsupervised Competitive Neural Network by Local Competitive Signals

Unsupervised competitive neural networks have been recognized as a powerful tool for pattern analysis, feature extraction and clustering analysis. The global competitive structures tend to critically depend on the number of elements in the networks and on the noise property of the space. In order to overcome these problems in this work is presented an unsupervised competitive neural network characterized by units with an adaptive threshold and local inhibitory interactions among its cells. Each neural unit is based on a modified competitive learning law in which the threshold changes in learning stage. It is shown that the proposed neuron is able, during the learning stage, to perform an automatic selection of patterns that belong to a cluster, moving towards its centroid. The properties of this network, are examined in a set of simulations adopting a data set composed of Gaussian mixtures.

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