Mind the Gaps: Measuring Visual Artifacts in Dimensionality Reduction

TL;DR

This paper introduces the Warping Index, a new metric for assessing the quality of dimensionality reduction visualizations by focusing on the preservation of empty regions to detect distortions and artifacts.

Contribution

The paper proposes the Warping Index, a novel metric that evaluates visual artifacts in DR plots by measuring how well empty regions are preserved, addressing limitations of existing PQMs.

Findings

Warps index effectively detects visual artifacts in DR plots.

Existing metrics overlook the importance of empty region preservation.

The new metric improves the assessment of projection quality.

Abstract

Dimensionality Reduction (DR) techniques are commonly used for the visual exploration and analysis of high-dimensional data due to their ability to project datasets of high-dimensional points onto the 2D plane. However, projecting datasets in lower dimensions often entails some distortion, which is not necessarily easy to recognize but can lead users to misleading conclusions. Several Projection Quality Metrics (PQMs) have been developed as tools to quantify the goodness-of-fit of a DR projection; however, they mostly focus on measuring how well the projection captures the global or local structure of the data, without taking into account the visual distortion of the resulting plots, thus often ignoring the presence of outliers or artifacts that can mislead a visual analysis of the projection. In this work, we introduce the Warping Index (WI), a new metric for measuring the quality of DR projections onto the 2D plane, based on the assumption that the correct preservation of empty regions between points is of crucial importance towards a faithful visual representation of the data.