Outlier-robust extreme learning machine for regression problems

Extreme learning machine (ELM), as one of the most useful techniques in machine learning, has attracted extensive attentions due to its unique ability for extremely fast learning. In particular, it is widely recognized that ELM has speed advantage while performing satisfying results. However, the presence of outliers may give rise to unreliable ELM model. In this paper, our study addresses the outlier robustness of ELM in regression problems. Based on the sparsity characteristic of outliers, this work proposes an outlier-robust ELM where the ℓ1-norm loss function is used to enhance the robustness. Specially, the fast and accurate augmented Lagrangian multiplier method is applied to guarantee the effectiveness and efficiency. According to the experiments on function approximation and some real-world applications, the proposed approach not only maintains the advantages from original ELM, but also shows notable and stable accuracy in handling data with outliers.