Dimension reduction is a technique that can compress given data and reduce noise. Recently, a dimension reduction technique on spheres, called spherical principal curves (SPC), has been proposed. SPC fits a curve that passes through the middle of data with a stationary property on spheres. In addition, a study of local principal geodesics (LPG) is considered to identify the complex structure of data. Through the description and implementation of various examples, this paper introduces an R package spherepc for dimension reduction of data lying on a sphere, including existing methods, SPC and LPG.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-016.zip
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For attribution, please cite this work as
Lee, et al., "The R Journal: spherepc: An R Package for Dimension Reduction on a Sphere", The R Journal, 2022
BibTeX citation
@article{RJ-2022-016,
author = {Lee, Jongmin and Kim, Jang-Hyun and Oh, Hee-Seok},
title = {The R Journal: spherepc: An R Package for Dimension Reduction on a Sphere},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-016},
doi = {10.32614/RJ-2022-016},
volume = {14},
issue = {1},
issn = {2073-4859},
pages = {167-181}
}