Attraction Forces Based Semi-Supervised Learning

In this paper we propose a new technique for Semi-Supervised Learning based on attraction forces. The main idea behind the SSL paradigm is to perform a classification task taking into account a few labeled instances and the information provided by many unlabeled instances. Essentially, the proposed technique considers each data instance as dimensionless points on a n-dimensional space and performs their dynamics accordingly to the resultant forces. The labeled points act as fixed attraction points whereas the unlabeled ones move towards them, whereby the unlabed instances are labeled through a label propagation mechanism when they approximate a defined neighborhood region around a fixed attraction point. The technique mainly takes into account two important SSL assumptions: smoothness and cluster. The results obtained from simulations performed on artificial datasets exhibit the effectiveness of the proposed method.