Improving the accuracy of nearest-neighbor classification using principled construction and stochastic sampling of training-set centroids
This work addresses accuracy limitations in nearest-neighbor classification for image datasets like MNIST and Fashion-MNIST, representing an incremental improvement.
The paper tackled the problem of limited accuracy in nearest-neighbor classification by increasing training-set coverage through coarse-graining and stochastic sampling of centroids, resulting in improved accuracy on MNIST and Fashion-MNIST datasets, elevating nearest-neighbor classification from mid- to upper-ranking among classical machine-learning techniques.
A conceptually simple way to classify images is to directly compare test-set data and training-set data. The accuracy of this approach is limited by the method of comparison used, and by the extent to which the training-set data cover configuration space. Here we show that this coverage can be substantially increased using coarse graining (replacing groups of images by their centroids) and stochastic sampling (using distinct sets of centroids in combination). We use the MNIST and Fashion-MNIST data sets to show that a principled coarse-graining algorithm can convert training images into fewer image centroids without loss of accuracy of classification of test-set images by nearest-neighbor classification. Distinct batches of centroids can be used in combination as a means of stochastically sampling configuration space, and can classify test-set data more accurately than can the unaltered training set. On the MNIST and Fashion-MNIST data sets this approach converts nearest-neighbor classification from a mid-ranking- to an upper-ranking member of the set of classical machine-learning techniques.