Adaptive Safe Semi-Supervised Extreme Machine Learning

Abstract

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 of unlabeled samples. Speci cally, the weights of manifold graph and its parameters, as well as the safety degrees of unlabeled samples will be optimized during the learning process rather than being calculated in advance. Finally, we then develop and implement a adaptive safe classi cation method based on the Adap-SaSSL framework, which is called adaptive safe semi-supervised extreme learning machine (AdSafe-SSELM). Experimental results on arti cial, benchmark and image datasets show that the performance of AdSafe-SSELM is effective and reliable compared to other algorithms.