Interpoint Distance Classification of High Dimensional Discrete Observations

Classification is a multivariate technique that is concerned with allocating new observations to two or more groups. We use interpoint distances to measure the closeness of the samples and construct new rules for high dimensional classification of discrete observations. Applicable to high dimensional data, the new method is non‐parametric and uses test‐based classification with permutation testing. We propose a modification of a test‐based rule to use relative values with respect to the training samples baseline. We compare the proposed rule with parametric methods, such as likelihood ratio rule and modified linear discriminate function, and non‐parametric techniques such as support vector machine, nearest neighbour and depth‐based classification, under multivariate Bernoulli, multinomial and multivariate Poisson distributions.