Data Driven Processing Via Two-Dimensional Spearman Correlation Analysis (2D-SCA)

This paper introduces an algorithm two-dimensional Spearman correlation analysis; the present algorithm is the extension of classical Spearman correlation analysis through algebraic solution for multivariate two-dimensional monotonic (linear or non-linear) multi-media datasets. In a way, two different images with nonlinearity challenges like different dimensions are processed with correspondence techniques such as reshaping images into 1D or vectors. Further, it can reduce dimension reduction along with quadratic algorithm complexity. Due to segmentation of matrices and tied rank ability of spearman correlation analysis. The implementation of proposed algorithm performs on four remarkable dataset. The results demonstration is helpful for researchers to choose finger image impression dataset with algorithm performance and sensors related techniques.