Density-oriented linear discriminant analysis

作者：

Highlights：

• LDA model has some challenges, e.g., sensitivity to outlier and singularity problem.

• Using the data density as prior knowledge for robustness against outliers.

• Bayesian risk is used to design the proposed method optimization problem.

• The proposed method can be employed for big data using the AdaBoost approach.

摘要

•LDA model has some challenges, e.g., sensitivity to outlier and singularity problem.•Using the data density as prior knowledge for robustness against outliers.•Bayesian risk is used to design the proposed method optimization problem.•The proposed method can be employed for big data using the AdaBoost approach.

论文关键词：Linear discriminant analysis (LDA),Outliers,Data density,Singularity problem,Adaboost,Robust LDA

论文评审过程：Received 12 March 2021, Revised 30 August 2021, Accepted 19 September 2021, Available online 30 September 2021, Version of Record 7 October 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115946