• A generally applicable dimensionality reduction method namely CKD has been proposed. • CKD leverages the differential characteristics of the data with respect to a reference data set. • Reference data can be obtained through an independent measurement or from the data themselves. • CKD shows superior performance over existing techniques. Dimensionality reduction and visualization of high dimensional data are fundamental tasks across scientific disciplines. Current dimensionality reduction methods rely on the use of some restrictive mathematical rules in projecting the data onto lower dimensions, which often leads to results of limited accuracy. Here, we present a generally applicable strategy of analyzing high-dimensional data by leveraging the differential characteristics of the data with respect to a reference data set. Depending on the problem, the reference data can be obtained either through an independent measurement or from the data themselves by finding a set of representative data points. The differentiating characteristics between the two sets of data are optimized by correlation transformation and scaling of the covariance matrices . We show the superior performance of the proposed method over existing techniques for a variety of data sets from different biomedical applications .