Gaussian Neural Networks Applied to the Cluster Analysis Problem
This paper describes a Gaussian neural network (GNN) applied to the cluster analysis problem. The GNN architecture is constituted by one layer of Gaussian units and one output unit which provides an estimation of the probability density function of the mixture. During the training of the network, a weighted competitive learning approach is used to estimate both the mean vector and the covariance matrix for each Gaussian function of the hidden units. The key problem with the GNN networks is the determination of the number of units in the hidden layer. This problem is solved by means of three information criteria. The interest of this approach lies in the adjusting of the number of units in an unsupervised context. Some results are reported and the performance of this approach is evaluated.
KeywordsHide Layer Radial Basis Function Network Hide Unit Output Unit Probabilistic Neural Network
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