A consensus-based semi-supervised growing neural gas

In this paper, we propose a new semi-supervised growing neural gas (GNG) model, named Consensus-Based Semi-Supervised GNG, or CSSGNG, in which both labeled and unlabeled data are used to train the network. In contrast to former adaptations of the GNG to semi-supervised classification, such as the SSGNG and OSSGNG models, the CSSGNG does not assign a single scalar label value to each neuron. Instead of the scalar, a vector containing the representativeness level of every class is associated with each neuron. Moreover, to propagate the labels among the neurons the CSSGNG employs a consensus approach. Computer experiments show that our model on average can deliver better classification results in comparison to the SSGNG and OSSGNG models.