cardinalR: Generating Interesting High-Dimensional Data Structures
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918256986554368 |
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| author | Gamage, Jayani P. Cook, Dianne Harrison, Paul Lydeamore, Michael Talagala, Thiyanga S. |
| author_facet | Gamage, Jayani P. Cook, Dianne Harrison, Paul Lydeamore, Michael Talagala, Thiyanga S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | cardinalR: Generating Interesting High-Dimensional Data Structures Gamage, Jayani P. Cook, Dianne Harrison, Paul Lydeamore, Michael Talagala, Thiyanga S. Methodology Applications 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. |
| title | cardinalR: Generating Interesting High-Dimensional Data Structures |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2512.18172 |