A new visual quality metric for Evaluating the performance of multidimensional projections
This work addresses the need for better evaluation metrics in visual analysis of multidimensional data, but it is incremental as it builds on existing methods like LAMP.
The paper tackles the problem of evaluating multidimensional projections by proposing a new visual quality metric based on human perception, combining silhouette coefficient, neighborhood preservation, and silhouette ratio, and shows it produces more precise results than previous metrics.
Multidimensional projections (MP) are among the most essential approaches in the visual analysis of multidimensional data. It transforms multidimensional data into two-dimensional representations that may be shown as scatter plots while preserving their similarity with the original data. Human visual perception is frequently used to evaluate the quality of MP. In this work, we propose to study and improve on a well-known map called Local Affine Multidimensional Projection (LAMP), which takes a multidimensional instance and embeds it in Cartesian space via moving least squares deformation. We propose a new visual quality metric based on human perception. The new metric combines three previously used metrics: silhouette coefficient, neighborhood preservation, and silhouette ratio. We show that the proposed metric produces more precise results in analyzing the quality of MP than other previously used metrics. Finally, we describe an algorithm that attempts to overcome a limitation of the LAMP method which requires a similar scale for control points and their counterparts in the Cartesian space.