Correlation classifiers based on data perturbation: New formulations and algorithms
作者：
Highlights：
• This paper develops a novel framework for a family of correlation classifiers.
• A family of correlation classifiers are designed from the pessimistic viewpoint under possible perturbation of data.
• The proximal majorization-minimization optimization (PMMO) is used to solve the proposed model.
• The convergence rate of the algorithm in terms of different criteria is given.
• The experiments have been conducted to demonstrate the effectiveness of the proposed approach.
摘要
•This paper develops a novel framework for a family of correlation classifiers.•A family of correlation classifiers are designed from the pessimistic viewpoint under possible perturbation of data.•The proximal majorization-minimization optimization (PMMO) is used to solve the proposed model.•The convergence rate of the algorithm in terms of different criteria is given.•The experiments have been conducted to demonstrate the effectiveness of the proposed approach.
论文关键词：Correlation classifiers,data perturbation,ϕ divergence,PMMO,Data classification
论文评审过程：Received 3 August 2018, Revised 29 October 2019, Accepted 10 November 2019, Available online 11 November 2019, Version of Record 16 November 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107106
