Discriminant component analysis via distance correlation maximization
作者：
Highlights：
• We propose a dimensionality reduction technique based on distance correlation.
• Our method maximizes the dependency between data samples and target variable.
• Kernel version of our method is also derived for non-linear problems.
• Our approach has a simple and closed-form solution.
• Our approach is computationally efficient.
摘要
•We propose a dimensionality reduction technique based on distance correlation.•Our method maximizes the dependency between data samples and target variable.•Kernel version of our method is also derived for non-linear problems.•Our approach has a simple and closed-form solution.•Our approach is computationally efficient.
论文关键词：Dimensionality reduction,Distance correlation (dCor),Kernel methods,Classification,Regression
论文评审过程：Received 24 October 2018, Revised 17 August 2019, Accepted 12 September 2019, Available online 13 September 2019, Version of Record 17 September 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107052
