IKPLS: Improved Kernel Partial Least Squares and Fast Cross-Validation Algorithms for Python with CPU and GPU Implementations Using NumPy and JAX

Python Submitted 25 January 2024 • Published 23 July 2024
Software repository Paper review Download paper Software archive
Review

Editor: @boisgera (all papers)
Reviewers: @parmentelat (all reviews), @basileMarchand (all reviews)

Authors

Ole-Christian Galbo Engstrøm (0000-0002-7906-4589), Erik Schou Dreier (0000-0001-9784-7504), Birthe Møller Jespersen (0000-0002-8695-1450), Kim Steenstrup Pedersen (0000-0003-3713-0960)

Citation

Engstrøm et al., (2024). IKPLS: Improved Kernel Partial Least Squares and Fast Cross-Validation Algorithms for Python with CPU and GPU Implementations Using NumPy and JAX. Journal of Open Source Software, 9(99), 6533, https://doi.org/10.21105/joss.06533

@article{Engstrøm2024, doi = {10.21105/joss.06533}, url = {https://doi.org/10.21105/joss.06533}, year = {2024}, publisher = {The Open Journal}, volume = {9}, number = {99}, pages = {6533}, author = {Ole-Christian Galbo Engstrøm and Erik Schou Dreier and Birthe Møller Jespersen and Kim Steenstrup Pedersen}, title = {IKPLS: Improved Kernel Partial Least Squares and Fast Cross-Validation Algorithms for Python with CPU and GPU Implementations Using NumPy and JAX}, journal = {Journal of Open Source Software} }
Copy citation string · Copy BibTeX  
Tags

PLS latent variables multivariate statistics cross-validation deep learning

Altmetrics
Markdown badge

 

License

Authors of JOSS papers retain copyright.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License

Journal of Open Source Software is an affiliate of the Open Source Inititative.

Journal of Open Source Software is part of Open Journals, which is a NumFOCUS-sponsored project.

Table of Contents
Public user content licensed CC BY 4.0 unless otherwise specified.
ISSN 2475-9066