A slice tour for finding hollowness in high-dimensional data

Ursula Laa; Dianne Cook; German Valencia

doi:10.1080/10618600.2020.1777140

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arXiv · 1910.10854

A slice tour for finding hollowness in high-dimensional data

Ursula Laa·Dianne Cook·German Valencia

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Abstract

Taking projections of high-dimensional data is a common analytical and visualisation technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualising data with concavities, or non-linear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots in interactive data visualisation. 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 non-linear structures in multivariate distributions.

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Ursula Laa, Dianne Cook, German Valencia. 2019-10-24. A slice tour for finding hollowness in high-dimensional data. https://doi.org/10.1080/10618600.2020.1777140

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