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Deconvolution When Classifying Noisy Data Involving Transformations

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  • Raymond Carroll
  • Aurore Delaigle
  • Peter Hall

Abstract

In 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.

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  • Raymond Carroll & Aurore Delaigle & Peter Hall, 2012. "Deconvolution When Classifying Noisy Data Involving Transformations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1166-1177, September.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:1166-1177
    DOI: 10.1080/01621459.2012.699793
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    Cited by:

    1. Nicoleta Serban & Ana-Maria Staicu & Raymond J. Carroll, 2013. "Multilevel Cross-Dependent Binary Longitudinal Data," Biometrics, The International Biometric Society, vol. 69(4), pages 903-913, December.

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