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The recent trends in collecting huge and diverse datasets have created a great challenge in data analysis. One of the characteristics of these gigantic datasets is that they often have significant amounts of redundancies. The use of very large multi-dimensional data will result in more noise, redundant data, and the possibility of unconnected data entities. To efficiently manipulate data represented...
A new technique for the Dimensionality Reduction of Multi-Dimensional Data is presented in this paper. This technique employs the theory of Copulas to estimate the multivariate joint probability distribution without constraints to specific types of marginal distributions of random variables that represent the dimensions of our Data. A Copulas-based model, provides a complete and scale-free description...
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