Sparse partial least-squares regression and its applications to high-throughput data analysis

Donghwan Lee, Woojoo Lee, Youngjo Lee, Yudi Pawitan

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


The partial least-squares (PLS) method is designed for prediction problems where the number of predictors is larger than the number of training samples. PLS is based on latent components that are linear combinations of all of the original predictors, so it automatically employs all predictors regardless of their relevance. This will potentially compromise its performance, but it will also make it difficult to interpret the result. In this paper, we propose a new formulation of the sparse PLS (SPLS) procedure to allow both sparse variable selection and dimension reduction. We use the standard L1-penalty and the unbounded penalty of [1]. We develop a computing algorithm for SPLS by modifying the nonlinear iterative partial least-squares (NIPALS) algorithm, and illustrate the method with an analysis of a cancer dataset. Through the numerical studies we find that our SPLS method generally performs better than the standard PLS and other existing methods in variable selection and prediction.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalChemometrics and Intelligent Laboratory Systems
Issue number1
StatePublished - 15 Nov 2011

Bibliographical note

Funding Information:
This research is partially supported by a grant from the Swedish Research Council and the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology ( 2010–0011372 ).


  • Lasso
  • Modeling
  • Prediction
  • Regression analyses
  • Variable selection


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