Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction

TL;DR

This paper provides a comprehensive guide for researchers and practitioners to navigate the extensive literature on dimensionality reduction, improving the reliability of visual analytics and aiding in selecting appropriate methods.

Contribution

It introduces a structured guide based on literature classification to help users assess their expertise and find relevant papers for reliable DR-based visual analytics.

Findings

Expert interviews validate the guide's usefulness.

The guide helps users identify suitable literature based on their expertise.

It addresses the challenge of navigating extensive DR research.

Abstract

Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual analytics with DR. Relying on the previous classification of the relevant literature, our guide helps both practitioners to (1) assess their current DR expertise and (2) identify papers that will further enhance their understanding. Interview studies with three experts in DR and data visualizations validate the significance, comprehensiveness, and usefulness of our guide.