A new visual quality metric for Evaluating the performance of multidimensional projections

TL;DR

This paper introduces a new visual quality metric for multidimensional projections that combines existing metrics and aligns better with human perception, improving evaluation accuracy of projection quality.

Contribution

It proposes a novel human perception-based quality metric for multidimensional projections and an algorithm to address scale limitations in LAMP.

Findings

The new metric outperforms previous metrics in evaluating projection quality.

The proposed algorithm mitigates scale limitations in LAMP.

Enhanced accuracy in visual analysis of multidimensional data.

Abstract

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.