Dimensionality reduction in data mining: A Copula approach
作者：
Highlights：
• Sampling-based dimensionality reduction technique.
• Eliminating linearly redundant combined dimensions.
• Providing a convenient way to generate correlated multivariate random variables.
• Maintaining the integrity of the original information.
• Reducing the dimension of data space without losing important information.
摘要
•Sampling-based dimensionality reduction technique.•Eliminating linearly redundant combined dimensions.•Providing a convenient way to generate correlated multivariate random variables.•Maintaining the integrity of the original information.•Reducing the dimension of data space without losing important information.
论文关键词：Data mining,Data pre-processing,Multi-dimensional sampling,Copulas,Dimensionality reduction
论文评审过程：Received 18 March 2016, Revised 24 May 2016, Accepted 28 July 2016, Available online 29 July 2016, Version of Record 3 August 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.07.041
