  This work is motivated by the problem of image mis-registration in remote
sensing and we are interested in determining the resulting loss in the accuracy
of pattern classification. A statistical formulation is given where we propose
to use data contamination to model and understand the phenomenon of image
mis-registration. This model is widely applicable to many other types of errors
as well, for example, measurement errors and gross errors etc. The impact of
data contamination on classification is studied under a statistical learning
theoretical framework. A closed-form asymptotic bound is established for the
resulting loss in classification accuracy, which is less than
$\epsilon/(1-\epsilon)$ for data contamination of an amount of $\epsilon$. Our
bound is sharper than similar bounds in the domain adaptation literature and,
unlike such bounds, it applies to classifiers with an infinite
Vapnik-Chervonekis (VC) dimension. Extensive simulations have been conducted on
both synthetic and real datasets under various types of data contamination,
including label flipping, feature swapping and the replacement of feature
values with data generated from a random source such as a Gaussian or Cauchy
distribution. Our simulation results show that the bound we derive is fairly
tight.
