Modelling contextual constraints in probabilistic relaxation for multi-class semi-supervised learning

This paper proposes a semi-supervised approach based on probabilistic relaxation theory. The algorithm performs a consistent multi-class assignment of labels according to the contextual information constraints. We start from a fully connected graph where each initial sample of the input data is a node of the graph and where only a few nodes have been labelled. A local propagation process is then performed by means of a support function where a new compatibility measure has been proposed. Contributions also include a comparative study of a wide variety of data sets with recent and well-known state-of-the-art algorithms for semi-supervised learning. The results have been provided by an analysis of their statistical significance. Our methodology has demonstrated a noticeably better performance in multi-class classification tasks. Experiments will also show that the proposed technique could be especially useful for applications such as hyperspectral image classification.