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arXiv · 2512.18172

cardinalR: Generating Interesting High-Dimensional Data Structures

Jayani P. Gamage·Dianne Cook·Paul Harrison·Michael Lydeamore·Thiyanga S. Talagala
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Abstract

Simulated high-dimensional data is useful for testing, validating, and improving algorithms used in dimension reduction, supervised and unsupervised learning. High-dimensional data is characterized by multiple variables that are dependent or associated in some way, such as linear, nonlinear, clustering or anomalies. Here we provide new methods for generating a variety of high-dimensional structures using mathematical functions and statistical distributions organized into the R package cardinalR. Several example data sets are also provided. These will be useful for researchers to better understand how different analytical methods work and can be improved, with a special focus on nonlinear dimension reduction methods. This package enriches the existing toolset of benchmark datasets for evaluating algorithms.

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Jayani P. Gamage, Dianne Cook, Paul Harrison, Michael Lydeamore, Thiyanga S. Talagala. 2025-12-20. cardinalR: Generating Interesting High-Dimensional Data Structures. https://arxiv.org/abs/2512.18172

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