Get access

Regularized partial least squares with an application to NMR spectroscopy

Authors

  • Genevera I. Allen,

    Corresponding author
    1. Department of Statistics, Rice University, Houston, TX, USA
    2. Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, USA
    3. Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
    • Department of Statistics, Rice University, Houston, TX, USA
    Search for more papers by this author
  • Christine Peterson,

    1. Department of Statistics, Rice University, Houston, TX, USA
    Search for more papers by this author
  • Marina Vannucci,

    1. Department of Statistics, Rice University, Houston, TX, USA
    Search for more papers by this author
  • Mirjana Maletić-Savatić

    1. Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, USA
    2. Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
    Search for more papers by this author

Abstract

High-dimensional data common in genomics, proteomics, and chemometrics often contains complicated correlation structures. Recently, partial least squares (PLS) and Sparse PLS methods have gained attention in these areas as dimension reduction techniques in the context of supervised data analysis. We introduce a framework for Regularized PLS by solving a relaxation of the SIMPLS optimization problem with penalties on the PLS loadings vectors. Our approach enjoys many advantages including flexibility, general penalties, easy interpretation of results, and fast computation in high-dimensional settings. We also outline extensions of our methods leading to novel methods for non-negative PLS and generalized PLS, an adoption of PLS for structured data. We demonstrate the utility of our methods through simulations and a case study on proton Nuclear Magnetic Resonance (NMR) spectroscopy data. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012

Get access to the full text of this article

Ancillary