Application of the R package cpfa to three real datasets.
Author: Matthew Asisgress
Package URL: https://CRAN.R-project.org/package=cpfa.
We apply R package cpfa (Asisgress, 2025) to three real datasets. First, we
apply the package to MNIST (LeCun, Cortes, and Burges, 1998; LeCun et al.,
2002)---showing how the package can be used to distinguish between images of the
digits 2 and 3. Next, we apply the package to Fashion MNIST (Xiao, Rasul, and
Vollgraf, 2017)---distinguishing among images of tops, trousers, and sandals.
Finally, we apply the package to VesselMNIST3D (Yang et al., 2020) from the
MedMNIST database (Yang et al., 2023; Yang, Shi, Ni, 2021). VesselMNIST3D
contains three-dimensional representations of blood vessels, and we distinguish
healthy blood vessels from aneurysm blood vessels. For this project, the source
file mcrd.Rmd contains original text and code used to produce the two reports
mcrd.pdf and mcrd.html, which both contain results.
This project is dual-licensed. All code and text, including the mcrd.Rmd
source file and the convert_npz_to_h5.py file, is licensed under the MIT
license. See the LICENSE file for details. Generated reports (mcrd.pdf and
mcrd.html), as derivative works of the MNIST dataset, are licensed under the
Creative Commons Attribution-ShareAlike 3.0 (CC BY-SA 3.0) license.
This project uses the R programming language (R Core Team, 2025). In addition, this project directly uses five R packages found on The Comprehensive R Archive Network (CRAN) including: cpfa (Asisgress, 2025), dslabs (Irizarry and Gill, 2025), keras3 (Kalinowski, Allaire, and Chollet, 2025), rhdf5 (Fischer, Smith, and Pau, 2025), and plotly (Sievert, 2020). Note that R package cpfa depends on R package multiway (Helwig, 2025), which itself depends on CMLS (Helwig, 2025).
This project uses three datasets. First, it uses MNIST (LeCun, Cortes, and Burges, 1998; LeCun et al., 2002). Yann LeCun and Corinna Cortes hold the copyright of the MNIST dataset, which is a derivative work from the original NIST datasets. MNIST is made available under the terms of the CC BY-SA 3.0 license, which can be viewed here: https://creativecommons.org/licenses/by-sa/3.0/deed.en. Moreover, this project uses Fashion MNIST (Xiao, Rasul, and Vollgraf, 2017). Zalando SE holds the copyright for Fashion MNIST, and the associated MIT license for this dataset can be found at the end of this README. Finally, this project uses VesselMNIST3D (Yang et al., 2020) from the MedMNIST database (Yang et al., 2023; Yang, Shi, Ni, 2021). VesselMNIST3D is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license, which can be viewed here: https://creativecommons.org/licenses/by/4.0/deed.en.
MNIST and Fashion MNIST are downloaded when mcrd.Rmd runs. VesselMNIST3D is
provided in the project GitHub repository in both its original .npz file format
and an .h5 file format. The Python script 'convert_npz_to_h5.py' was used to
convert VesselMNIST3D from .npz to .h5. This script is made available in the
project repository. When mcrd.Rmd runs, it subsets a given dataset, builds a
classification rule between data and class labels, and provides summaries of
these analyses. Results can be viewed within output files mcrd.pdf or
mcrd.html.
Asisgress, M. (2025). cpfa: Classification with Parallel Factor Analysis. R package version 1.2-3, https://CRAN.R-project.org/package=cpfa.
Fischer, B., Smith, M., and Pau, G. (2025). rhdf5: R Interface to HDF5. R package version 2.52.1, https://bioconductor.org/packages/rhdf5.
Helwig, N. (2025). CMLS: Constrained Multivariate Least Squares. R package version 1.1, https://CRAN.R-project.org/package=CMLS.
Helwig, N. (2025). multiway: Component Models for Multi-Way Data. R package version 1.0-7, https://CRAN.R-project.org/package=multiway.
Irizarry, R., Gill, A. (2025). dslabs: Data Science Labs. R package version 0.9.0, https://CRAN.R-project.org/package=dslabs.
Kalinowski, T., Allaire, J., and Chollet, F. (2025). keras3: R Interface to 'Keras'. R package version 1.4.0, https://CRAN.R-project.org/package=keras3.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (2002). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791.
LeCun, Y., Cortes, C., and Burges, C. (1998). The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/.
R Core Team (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.
Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.
Yang, J., Shi, R., and Ni, B. (April 2021). Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 191-195). IEEE.
Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., ... and Ni, B. (2023). Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data, 10(1), 41.
Yang, X., Xia, D., Kin, T., and Igarashi, T. (2020). Intra: 3d intracranial aneurysm dataset for deep learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2656-2666).
The MIT License (MIT) Copyright © 2017 Zalando SE, https://tech.zalando.com Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.