Online semi-supervised extreme learning machine based on manifold regularization

In this paper, with the help of the rules of block matrix multiplication, an online semi-supervised extreme learning machine (OSS-ELM) was proposed according to semi-supervised extreme learning machine (SS-ELM) based on manifold regularization. By the analysis of the manifoldregularization term of the objective function of SS-ELM, a kind of approximation algorithm of OSS-ELM named OSS-ELM (buffer) was proposed to avoid running out of memory in the process of online learning. The linear relationship between the sample number and the cumulative running time of the OSS-ELM (buffer) was revealed in the experiments using Abalone and the relative deviation of the generalization ability of the OSS-ELM and the SS-ELM is less than 1% in 9 public data sets, which show that the OSS-ELM (buffer) not only solves the problem of limited memory, but also improves the speed of online learning while keeping the generalization ability of SS-ELM. This proves that the OSS-ELM (buffer) can be effectively applied to online semi-supervised learning. ©, 2015, Shanghai Jiao Tong University. All right reserved.