Semi-supervised learning from imperfect data through particle cooperation and competition

In machine learning study, semi-supervised learning has received increasing interests in the last years. It is applied to classification problems where only a small portion of the data points is labeled. In these situations, the reliability of these labels is extremely important because it is common to have mislabeled samples in a data set and these may propagate their wrong labels to a large portion of the data set, resulting in major classification errors. In spite of its importance, wrong label propagation in semi-supervised learning has received little attention from researchers. In this paper we propose a particle walk semi-supervised learning method with both competitive and cooperative mechanisms. Then we study error propagation by applying the proposed model in modular networks. We show that the model is robust against mislabeled samples and it can produce good classification results even in the presence of considerable proportion of mislabeled data. Moreover, our numerical analysis uncover a critical point of mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. These studies have practical importance to design secure and robust machine learning techniques.