Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction

2025-01-17

AI Summary

Dimensionality reduction (DR) visualization of high-dimensional data frequently suffers from projection distortion, undermining analytical reliability and decision-making accuracy. Method: We systematically reviewed 133 publications and—through bibliometric analysis, workflow modeling, taxonomy development, and validation by five HCI/Vis experts—introduced the first reliability-oriented DR visualization workflow model and a multi-dimensional reliability attribution taxonomy, exposing a structural imbalance privileging technical advancement over interpretability. Contribution/Results: We propose a human–AI collaborative analysis framework and a comprehensive reliability problem classification system. Our work identifies critical gaps in current reliability research, shifting community focus from algorithmic novelty toward enhanced explainability, systematic evaluation, and human-centered integration. This advances foundational understanding and practice in trustworthy DR visualization.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from factors such as inherent distortions in DR projections. This unreliability can lead to analytic insights that misrepresent the underlying data, potentially resulting in misguided decisions. To tackle these reliability challenges, we review 133 papers that address the unreliability of visual analytics using DR. Through this review, we contribute (1) a workflow model that describes the interaction between analysts and machines in visual analytics using DR, and (2) a taxonomy that identifies where and why reliability issues arise within the workflow, along with existing solutions for addressing them. Our review reveals ongoing challenges in the field, whose significance and urgency are validated by five expert researchers. This review also finds that the current research landscape is skewed toward developing new DR techniques rather than their interpretation or evaluation, where we discuss how the HCI community can contribute to broadening this focus.

Problem

• High-dimensional data
• Dimensionality reduction
• Data distortion

Innovation

• Dimensionality Reduction
• Reliability Improvement
• Human-Computer Collaboration