  From linear classifiers to neural networks, image classification has been a
widely explored topic in mathematics, and many algorithms have proven to be
effective classifiers. However, the most accurate classifiers typically have
significantly high storage costs, or require complicated procedures that may be
computationally expensive. We present a novel (nonlinear) classification
approach using truncation of local tensor singular value decompositions (tSVD)
that robustly offers accurate results, while maintaining manageable storage
costs. Our approach takes advantage of the optimality of the representation
under the tensor algebra described to determine to which class an image
belongs. We extend our approach to a method that can determine specific
pairwise match scores, which could be useful in, for example, object
recognition problems where pose/position are different. We demonstrate the
promise of our new techniques on the MNIST data set.
