Regularized partial least squares with an application to NMR spectroscopy

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dc.citation.firstpage302
dc.citation.issueNumber4
dc.citation.journalTitleStatistical Analysis and Data Mining
dc.citation.lastpage314
dc.citation.volumeNumber6
dc.contributor.authorAllen, Genevera I.
dc.contributor.authorPeterson, Christine
dc.contributor.authorVannucci, Marina
dc.contributor.authorMaletic-Savatic, Mirjana
dc.date.accessioned2014-10-07T21:52:28Z
dc.date.available2014-10-07T21:52:28Z
dc.date.issued2013
dc.description.abstractHigh-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.
dc.identifier.citationAllen, Genevera I., Peterson, Christine, Vannucci, Marina, et al.. "Regularized partial least squares with an application to NMR spectroscopy." <i>Statistical Analysis and Data Mining,</i> 6, no. 4 (2013) John Wiley & Sons, Inc.: 302-314. http://dx.doi.org/10.1002/sam.11169.
dc.identifier.doihttp://dx.doi.org/10.1002/sam.11169
dc.identifier.urihttps://hdl.handle.net/1911/77433
dc.language.isoeng
dc.publisherJohn Wiley & Sons, Inc.
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyighted by Wiley.
dc.subject.keywordsparse PLS
dc.subject.keywordsparse PCA
dc.subject.keywordNMR spectroscopy
dc.subject.keywordgeneralized PCA
dc.subject.keywordnon-negative PLS
dc.subject.keywordgeneralized PLS
dc.titleRegularized partial least squares with an application to NMR spectroscopy
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpost-print
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