Explaining dimensionality reduction results using Shapley values
作者：
Highlights：
• Clusters of dimensionality reduction results are interpreted using Shapley values.
• Summary visualizations convey the decisions of dimensionality reduction processes.
• Shapley values can reveal ubiquitous information of medical datasets.
摘要
•Clusters of dimensionality reduction results are interpreted using Shapley values.•Summary visualizations convey the decisions of dimensionality reduction processes.•Shapley values can reveal ubiquitous information of medical datasets.
论文关键词：Explainability,Dimensionality reduction,Shapley values,Visualization
论文评审过程：Received 17 May 2020, Revised 28 March 2021, Accepted 8 April 2021, Available online 16 April 2021, Version of Record 3 May 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115020
