Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images
作者：
Highlights：
• A new MIMR criterion is proposed for unsupervised feature selection.
• MIMR can select more informative and distinctive features.
• Many classical criteria and MIMR can be unified into the same framework.
• The theoretical advantage of MIMR over many classical criteria is given.
• Feature selection problem is solved by combinatorial optimization with a new CSA.
摘要
•A new MIMR criterion is proposed for unsupervised feature selection.•MIMR can select more informative and distinctive features.•Many classical criteria and MIMR can be unified into the same framework.•The theoretical advantage of MIMR over many classical criteria is given.•Feature selection problem is solved by combinatorial optimization with a new CSA.
论文关键词：Unsupervised feature selection,Hyperspectral images,Maximum information and minimum redundancy,Information theory,Clonal selection algorithm
论文评审过程：Received 29 July 2014, Revised 16 June 2015, Accepted 22 August 2015, Available online 2 September 2015, Version of Record 27 November 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.08.018
