A Slice Tour for Finding Hollowness in High-Dimensional Data. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Laa, Ursula; Cook, Dianne; Valencia, German
• Is Part Of: Journal of computational and graphical statistics. Volume 29:Issue 3(2020)
• Page Start: 681
• Page End: 687
• Publication Date: 2020-07-02
• Subjects: Data visualization -- Dynamic graphics -- Grand tour -- Multivariate data -- Statistical computing -- Statistical graphics; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2020.1777140
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: 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.