A Simple Kernel-based Nearest Neighbor Method for the MNIST Database of Handwritten Digits Classification

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.