Making nonlinear manifold learning models interpretable: The manifold grand tour
作者：
Highlights：
• Smooth nonlinear topographic maps of the data distribution to guide a Grand Tour visualisation.
• Prioritisation of data linear views that are most consistent with data structure in the maps.
• Useful visualisations that cannot be obtained by other more classical approaches.
摘要
•Smooth nonlinear topographic maps of the data distribution to guide a Grand Tour visualisation.•Prioritisation of data linear views that are most consistent with data structure in the maps.•Useful visualisations that cannot be obtained by other more classical approaches.
论文关键词：Manifold learning,Grand tour,Data visualisation,Nonlinear dimensionality reduction,Linear projections
论文评审过程：Received 20 May 2014, Revised 22 July 2015, Accepted 24 July 2015, Available online 31 July 2015, Version of Record 29 August 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.054
