Using Interacting Forces to Perform Semi-supervised Learning

Semi-Supervised Learning (SSL) is a learning paradigm in which the classification task is performed by taking into account just a few labeled instances. The unlabeled instances also participate in the process, but by providing additional information about the dataset. In this paper, a new semi-supervised technique based on interacting forces is proposed. Both labeled and unlabeled instances play different roles in the proposed mechanism: the labeled instances perform attraction forces over the unlabeled instances to accomplish label propagation. Inside a defined neighborhood, a label in able to propagates to an unlabeled instance. The technique mainly takes into account two important SSL assumptions: smoothness and cluster. Results obtained from simulations performed on artificial and real datasets exhibit the effectiveness of the proposed method.