A Slice Tour for Finding Hollowness in High-Dimensional Data

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Taking projections of high-dimensional data is a common analytical and visualization technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualizing data with concavities, or nonlinear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots in interactive data visualization. This short technical note describes a simple approach for slicing in the orthogonal space of projections obtained when running a tour, thus presenting the viewer with an interpolated sequence of sliced projections. The method has been implemented in R as an extension to the tourr package, and can be used to explore for concave and nonlinear structures in multivariate distributions. Supplementary materials for this article are available online.

Original languageEnglish
Number of pages7
JournalJournal of Computational and Graphical Statistics
Publication statusAccepted/In press - 16 Jul 2020


  • Data visualization
  • Dynamic graphics
  • Grand tour
  • Multivariate data
  • Statistical computing
  • Statistical graphics

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