From 3fb10520395f7e8ae8f3ab31ce454f7df2125481 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ole=20Engstr=C3=B8m?= Date: Sat, 22 Jun 2024 18:32:23 +0200 Subject: [PATCH] Another, minor update of paper.md in relation to #29. --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 9f112ec..621ff76 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,7 +37,7 @@ bibliography: paper.bib --- # Summary -The `ikpls` software package provides fast and efficient tools for PLS (partial least squares) modeling. This package is designed to help researchers and practitioners handle PLS modeling faster than previously possible - particularly on large datasets. The PLS implementations in `ikpls` use the fast IKPLS (Improved Kernel PLS) algorithms [@dayal1997improved], providing a substantial speedup compared to scikit-learn's [@scikit-learn] PLS implementation, which is based on NIPALS (Nonlinear Iterative Partial Least Squares) [@wold1966estimation]. The `ikpls` package also offers an implementation of IKPLS combined with the fast cross-validation algorithms by Engstrøm [@engstrøm2024shortcutting], significantly accelerating cross-validation of PLS models - especially when using a large number of cross-validation splits. +The `ikpls` software package provides fast and efficient tools for PLS (partial least squares) modeling. This package is designed to help researchers and practitioners handle PLS modeling faster than previously possible - particularly on large datasets. The PLS implementations in `ikpls` use the fast IKPLS (Improved Kernel PLS) algorithms [@dayal1997improved], providing a substantial speedup compared to scikit-learn's [@scikit-learn] PLS implementation, which is based on NIPALS (Nonlinear Iterative Partial Least Squares) [@wold1966estimation]. The `ikpls` package also offers an implementation of IKPLS combined with the fast cross-validation algorithm by Engstrøm [@engstrøm2024shortcutting], significantly accelerating cross-validation of PLS models - especially when using a large number of cross-validation splits. `ikpls` offers NumPy-based CPU and JAX-based CPU/GPU/TPU implementations. The JAX implementations are also differentiable, allowing seamless integration with deep learning techniques. This versatility enables users to handle diverse data dimensions efficiently.