A visualization metric for dimensionality reduction

Data visualization of high-dimensional data is possible through the use of dimensionality reduction techniques. However, in deciding which dimensionality reduction techniques to use in practice, quantitative metrics are necessary for evaluating the results of the transformation and visualization of the lower dimensional embedding. In this paper, we propose a manifold visualization metric based on the pairwise correlation of the geodesic distance in a data manifold. This metric is compared with other metrics based on the Euclidean distance, Mahalanobis distance, City Block metric, Minkowski metric, cosine distance, Chebychev distance, and Spearman distance. The results of applying different dimensionality reduction techniques on various types of nonlinear manifolds are compared and discussed. Our experiments show that our proposed metric is suitable for quantitatively evaluating the results of the dimensionality reduction techniques if the data lies on an open planar nonlinear manifold. This has practical significance in the implementation of knowledge-based visualization systems and the application of knowledge-based dimensionality reduction methods.

► A quantitative metric for visualizing the results of dimensionality reduction is proposed. ► The visualization metric is based on the geodesic distances of the original data. ► This metric is compared with other metrics based on Euclidean, Mahalanobis, etc. ► The results of different dimensionality reduction techniques on nonlinear manifolds are compared and discussed. ► The metric is suitable for quantitatively evaluating the results on an open planar nonlinear manifold.