Data-Driven Sparse Partial Least Squares - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Statistical Analysis and Data Mining Year : 2022

Data-Driven Sparse Partial Least Squares

Abstract

In the supervised high dimensional settings with a large number of variables and a low number of individuals, variable selection allows a simpler interpretation and more reliable predictions. That subspace selection is often managed with supervised tools when the real question is motivated by variable prediction. We propose a Partial Least Square (PLS) based method, called data-driven sparse PLS (ddsPLS), allowing variable selection both in the covariate and the response parts using a single hyper-parameter per component. The subspace estimation is also performed by tuning a number of underlying parameters. The ddsPLS method is compared to existing methods such as classical PLS and two well established sparse PLS methods through numerical simulations. The observed results are promising both in terms of variable selection and prediction performance. This methodology is based on new prediction quality descriptors associated with the classical R 2 and Q 2 and uses bootstrap sampling to tune parameters and select an optimal regression model.
Fichier principal
Vignette du fichier
ddsPLS_tout_seul_no_bib_Pour_HAL_Inria.pdf (1.03 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03368956 , version 1 (07-10-2021)

Identifiers

Cite

Hadrien Lorenzo, Olivier Cloarec, Rodolphe Thiébaut, Jérôme Saracco. Data-Driven Sparse Partial Least Squares. Statistical Analysis and Data Mining, 2022, 15 (2), pp.264-282. ⟨10.1002/sam.11558⟩. ⟨hal-03368956⟩
113 View
468 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More