Adaptive robust learning framework for twin support vector machine classification
作者：
Highlights：
• A correntropy-based generalized robust distance metric is proposed, called correntropy induced metric (CIM).
• A robust loss function is proposed, namely, adaptive capped Lθε-loss.
• Some important and interesting properties of the Lθε-loss and CIM are demonstrated.
• A adaptive robust twin support vector machine (ARTSVM) is proposed based on CIM and Lθε-loss.
• DC programming technique is used to solve the ARTSVM.
• Numerical experiments under different noises show that the proposed ARTSVM is effective and more robust to outliers.
摘要
•A correntropy-based generalized robust distance metric is proposed, called correntropy induced metric (CIM).•A robust loss function is proposed, namely, adaptive capped Lθε-loss.•Some important and interesting properties of the Lθε-loss and CIM are demonstrated.•A adaptive robust twin support vector machine (ARTSVM) is proposed based on CIM and Lθε-loss.•DC programming technique is used to solve the ARTSVM.•Numerical experiments under different noises show that the proposed ARTSVM is effective and more robust to outliers.
论文关键词：Robustness,Correntropy,Distance metric,Twin support vector machine,DC programming
论文评审过程：Received 12 March 2020, Revised 17 September 2020, Accepted 13 October 2020, Available online 14 October 2020, Version of Record 2 November 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106536
