Classification of Multi-dimensional Distributions Using Order Statistics Criteria
This paper proposes a novel classification paradigm in which the properties of the Order Statistics (OS) have been used to perform an optimal/near-optimal solution for multi-dimensional problems. In our initial works in  and , we proposed the foundational theory of CMOS, Classification by the Moments of Order Statistics, for some uni-dimensional symmetric and asymmetric distributions of the exponential family. In this paper, we generalize those results for various multidimensional distributions. The strategy is analogous to a Naïve-Bayes’ approach, although it, really, is of an anti-Naïve-Bayes’ paradigm.We provide here the analytical and experimental results for the two-dimensional Uniform, Doubly-exponential and Gaussian and Rayleigh distributions, and also clearly specify the way by which one should extend the results for higher dimensions.
KeywordsClassification using Order Statistics (OS) Moments of OS
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