In this paper we propose a series of simple and fast kernel-based nearest
neighbor classification algorithms based on CW-SSIM index for the MNIST
database, which appears to be effective and reliable tools for the MNIST
Database of Handwritten Digits Classification. Given that the CW-SSIM index
provides a powerful similarity measure between two misaligned images and there
are sufficient training examples in the MNIST database, we obtain amazing
results with employing the simplest k-NN model, i.e., only using most similar
images to classify test examples.