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

2025-06-10

AI Summary

Dimensionality reduction (DR) of high-dimensional data in visual analytics is highly susceptible to distortion from the original data, compromising result reliability; moreover, the abundance of existing DR methods—coupled with a lack of systematic guidance—hinders efficient onboarding for novices and researchers alike. Method: We propose the first literature taxonomy–based, adaptive DR learning pathway guide, integrating a methodological DR classification framework, expert interviews from the DR and visualization communities, and empirical validation to establish a structured, actionable literature navigation paradigm. Contribution/Results: The guide enables users to precisely locate progressively advanced literature aligned with their expertise level. Evaluated by three domain experts in DR and visual analytics, it demonstrates strong comprehensiveness, practicality, and pedagogical efficacy, significantly lowering the entry barrier for reliability-driven DR applications.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Dimensionality Reduction/Feature Selection
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Addressing unreliability in DR-based visual analytics
• Guiding novices through diverse DR literature
• Helping practitioners assess and enhance DR expertise

Innovation

• Guide for reliable DR visual analytics
• Classifies literature to enhance understanding
• Validated by experts in DR visualization