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 classification and fixed during the classification learning process, which results independent with the subsequent classification. Aiming at the above problems, we propose a unified adaptive safe semi-supervised learning (Adap-SaSSL) framework. This framework adaptively constructs a manifold graph while adaptively calculating the safety degrees of unlabeled samples. Specifically, 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 classification method based on the Adap-SaSSL framework, which is called adaptive safe semi-supervised extreme learning machine (AdSafe-SSELM). Experimental results on artificial, benchmark and image datasets show that the performance of AdSafe-SSELM is effective and reliable compared to other algorithms.