Deconvolution When Classifying Noisy Data Involving Transformations.

  title={Deconvolution When Classifying Noisy Data Involving Transformations.},
  author={Raymond J. Carroll and A. Delaigle and Peter W. Hall},
  journal={Journal of the American Statistical Association},
  volume={107 499},
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… CONTINUE READING

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