Dimension reduction by identifying and removing redundant variables using copula function

In today's world, rapid developments in science and engineering are increasingly adding up to larger amounts of data; as a result, numerous problems have emerged in the analysis of big data. Hence, data dimensionality reduction can accelerate data analysis and even yield better results without losing any useful data.  A copula represents an appropriate model of dependence to compare multivariate distributions and better detect the relationships of data. Therefore, a copula is employed in this study to identify and delete noisy data  from the original data.  Then, it is compared to  the principal component analysis to show its superiority.

Keywords:

Gaussian copula function (normal) , Classification , Principal component analysis method (PCA) , data analysis , Parkinson’s Disease