BBAuthors: Ma, J.; Advisor: -; Participants: Yuan, C. (2019)
Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent learning framework. However, the performance of SSL heavily depends on the construction of manifold graph and the safety degrees of unlabeled samples. Due to the construction of manifold graph and safety degrees of unlabeled samples are usually pre-construct before classi cation and xed during the classi cation learning process, which results independent with the subsequent classi cation. Aiming at the above problems, we propose a uni ed adaptive safe semi-supervised learning (Adap-SaSSL) framework. This framework adaptively constructs a manifold graph while adaptively calculating the safety degrees ... 
