Adaptive safety degree-based safe semi-supervised learning

Abstract

Recently, safe semi-supervised learning has attracted more and more attention in the machine learning field. Many methods are introduced to safely exploit unlabeled data by designing different safe mechanisms. However, they assume that the risk or safety degrees are equal for all unlabeled data. In this paper, we propose an adaptive safe semi-supervised learning framework where the safety degrees of different unlabeled data are different and adaptively computed. In this framework, a safety degree-based tradeoff term between supervised learning (SL) and semi-supervised learning (SSL) is incorporated into the objective function of SSL. Then the optimal problem is solved by using an alternating iterative strategy. In particular, we utilize Regularized Least Squares (RLS) and Laplacian RLS (LapRLS) for SL and SSL, respectively. Our experimental results on several datasets demonstrate that the performance of our algorithm is never significantly inferior to that of RLS and LapRLS and show the effectiveness of our proposed safety mechanism.