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    Regularized partial least squares with an application to NMR spectroscopy

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    Author
    Allen, Genevera I.
    Peterson, Christine
    Vannucci, Marina
    Maleti?-Savati?, Mirjana
    Date
    2013
    Citation
    Allen, Genevera I., Peterson, Christine, Vannucci, Marina, et al.. "Regularized partial least squares with an application to NMR spectroscopy." Statistical Analysis and Data Mining, 6, no. 4 (2013) 302-314. http://dx.doi.org/10.1002/sam.11169.
    Published Version
    http://dx.doi.org/10.1002/sam.11169
    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.
    Keyword
    sparse PLS; sparse PCA; NMR spectroscopy; generalized PCA; non-negative PLS; More... generalized PLS Less...
    Type
    Journal article
    Citable link to this page
    http://hdl.handle.net/1911/77433
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    • Faculty Publications [3507]
    • Statistics Publications [100]

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    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892