2019/03/26 by Hongwei Dong, Liming Yang, Dong, Hongwei +1
Computer Science · Mathematics · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1903.11202
openalex publication_date 2019/03/26 · arxiv created 2020/06/02 · arxiv updated 2020/06/03 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Least squares kernel based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called ℓs-loss, is proposed in this paper. With the support of novel loss function, two kernel based regressors are constructed by replacing the ℓ2-loss in LS-SVR and ELM with the proposed ℓs-loss for better noise robustness. Important properties of ℓs-loss, including robustness, asymmetry and asymptotic approximation behaviors, are verified theoretically. Moreover, iteratively reweighted least squares (IRLS) is utilized to optimize and interpret the proposed methods from a weighted viewpoint. The convergence of the proposal are proved, and detailed analyses of robustness are given. Experiments on both artificial and benchmark datasets confirm the validity of the proposed methods.