Classification of High Dimensional Discrete Observations
Open AccessClassification is a multivariate technique that is concerned with allocating new observations to two or more groups. A common method of classification, the normal theory linear discriminate function is not applicable to high dimensional data or for discrete multivariate distributions. We use interpoint distances to measure the closeness of two samples, discuss their properties and use them to construct new rules for high dimensional discrete classification. We compare the proposed rules with the likelihood ratio test and data depths classifications for multivariate Bernoulli, multinomial, and multivariate Poisson distributions.
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