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.
|Keywords||Classification using order statistics (OS), Moments of OS|
|Series||Advances in Intelligent Systems and Computing|
Thomas, A. (A.), & Oommen, J. (2013). Classification of multi-dimensional distributions using order statistics Criteria. Advances in Intelligent Systems and Computing. doi:10.1007/978-3-319-00969-8_2