دستگاه های یادگیری افراطی ثابت برای رگرسیون با استفاده از حداقل مربعات تکرار شده با وزن مجدد

Extreme learning machine (ELM) for regression has been used in many fields because of its easy-implementation, fast training speed and good generalization performance. However, basic ELM with ℓ2-norm loss function is sensitive to outliers. Recently, ℓ1-norm loss function and Huber loss function have been used in ELM to enhance the robustness. However, the ℓ1-norm loss function and the Huber loss function can also be effected by outliers because of their linear correlation with the errors. Moreover, existing robust ELM methods only use ℓ2-norm regularization or have no regularization term. In this study, we propose a unified model for robust regularized ELM regression using iteratively reweighted least squares (IRLS), and call it RELM-IRLS. We perform a comprehensive study on the robust loss function and regularization term for robust ELM regression. Four loss functions (i.e., ℓ1-norm, Huber, Bisquare and Welsch) are used to enhance the robustness, and two types of regularization (ℓ2-norm and ℓ1-norm) are used to avoid overfitting. Experiments show that our proposed RELM-IRLS with ℓ2-norm and ℓ1-norm regularization is stable and accurate for data with 0∼40% outlier levels, and that RELM-IRLS with ℓ1-norm regularization can obtain a compact network because of the highly sparse output weights of the network.