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dc.contributor.authorCarroll, Raymond
dc.contributor.authorDelaigle, Aurore
dc.contributor.authorHall, Peter
dc.date.accessioned2016-02-25T12:58:56Z
dc.date.available2016-02-25T12:58:56Z
dc.date.issued2012-09
dc.identifier.citationCarroll R, Delaigle A, Hall P (2012) Deconvolution When Classifying Noisy Data Involving Transformations. Journal of the American Statistical Association 107: 1166–1177. Available: http://dx.doi.org/10.1080/01621459.2012.699793.
dc.identifier.issn0162-1459
dc.identifier.issn1537-274X
dc.identifier.pmid23606778
dc.identifier.doi10.1080/01621459.2012.699793
dc.identifier.urihttp://hdl.handle.net/10754/597922
dc.description.abstractIn the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.
dc.description.sponsorshipRaymond Carroll is Head, Department of Statistics, Texas A&M University, College Station, TX 77843-3143 (E-mail: carroll@stat.tamu.edu). Aurore Delaigle is Associate Professor (E-mail: a.delaigle@ms.unimelb.edu.au) and Peter Hall is Professor (E-mail: halpstat@ms.unimelb.edu.au), Department of Mathematics and Statistics, University of Melbourne, VIC 3010, Australia. Carroll's research was supported by a grant from the National Cancer Institute (R37-CA057030) and in part by award number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST) and by the National Science Foundation (DMS-0914951). Delaigle's research was supported by grants and a Queen Elizabeth II Fellowship from the Australian Research Council, and Hall's research was supported by a Federation Fellowship, a Laureate Fellowship, and grants from the Australian Research Council.
dc.publisherInforma UK Limited
dc.subjectFourier transform
dc.subjectcross-validation
dc.subjectSpatial Data
dc.subjectCentroid Classifier
dc.subjectInverse Transform
dc.titleDeconvolution When Classifying Noisy Data Involving Transformations
dc.typeArticle
dc.identifier.journalJournal of the American Statistical Association
dc.identifier.pmcidPMC3630802
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX 77843-3143.


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