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A conceptually simple way to classify images is to directly compare test-setdata and training-set data. The accuracy of this approach is limited by themethod of comparison used, and by the extent to which the training-set datacover configuration space. Here we show that this coverage can be substantiallyincreased using coarse graining (replacing groups of images by their centroids)and stochastic sampling (using distinct sets of centroids in combination). Weuse the MNIST and Fashion-MNIST data sets to show that a principledcoarse-graining algorithm can convert training images into fewer imagecentroids without loss of accuracy of classification of test-set images bynearest-neighbor classification. Distinct batches of centroids can be used incombination as a means of stochastically sampling configuration space, and canclassify test-set data more accurately than can the unaltered training set. Onthe MNIST and Fashion-MNIST data sets this approach converts nearest-neighborclassification from a mid-ranking- to an upper-ranking member of the set ofclassical machine-learning techniques.

Improving the accuracy of nearest-neighbor classification using principled construction and stochastic sampling of training-set centroids