Robust semi-supervised extreme learning machine
作者：
Highlights：
• To effectively exploit the geometric information embedded in unlabeled datavia the manifold regularization term.
• To have a good ability to reduce the negative influence of outliers by exploiting thenon-convex loss function.
• To demonstrate the robustness of RSS-ELM in theory from the perspective of reweighted.
• To be efficiently solved by the well known CCCP method.
• Validity is investigated by comparing it with several related algorithms on multiple image datasets and UCI datasets.
摘要
•To effectively exploit the geometric information embedded in unlabeled datavia the manifold regularization term.•To have a good ability to reduce the negative influence of outliers by exploiting thenon-convex loss function.•To demonstrate the robustness of RSS-ELM in theory from the perspective of reweighted.•To be efficiently solved by the well known CCCP method.•Validity is investigated by comparing it with several related algorithms on multiple image datasets and UCI datasets.
论文关键词：Semi-supervised learning,Extreme learning machine,Robust,Non-convex loss function,CCCP
论文评审过程：Received 29 November 2017, Revised 28 June 2018, Accepted 30 June 2018, Available online 5 July 2018, Version of Record 10 September 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.06.029
