# Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study

arXiv: https://arxiv.org/abs/2110.02894 · HTML: https://mlsanity.com/paper/2110.02894/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2021.

## Built on by (most cited first, 27 of 27)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Uniform Manifold Approximation with Two-phase Optimization](https://mlsanity.com/paper/2205.00420/) (2022)
- [Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction](https://mlsanity.com/paper/2308.00278/) (2023)
- [Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization](https://mlsanity.com/paper/2307.11770/) (2023)
- [CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering](https://mlsanity.com/paper/2308.00284/) (2023)
- [Stop Misusing t-SNE and UMAP for Visual Analytics](https://mlsanity.com/paper/2506.08725/) (2025)
- [UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection](https://mlsanity.com/paper/2207.03809/) (2022)
- [A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations](https://mlsanity.com/paper/2407.17876/) (2024)
- [Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections](https://mlsanity.com/paper/2201.06379/) (2022)
- [Accelerating hyperbolic t-SNE](https://mlsanity.com/paper/2401.13708/) (2024)
- [TopoMap++: A faster and more space efficient technique to compute projections with topological guarantees](https://mlsanity.com/paper/2409.07257/) (2024)
- [ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models](https://mlsanity.com/paper/2605.23694/) (2026)
- ["Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches](https://mlsanity.com/paper/2206.07918/) (2022)
- [Navigating Perplexity: A linear relationship with the data set size in t-SNE embeddings](https://mlsanity.com/paper/2308.15513/) (2023)
- [Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids](https://mlsanity.com/paper/2403.20246/) (2024)
- [Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study](https://mlsanity.com/paper/2505.06702/) (2025)
- [Dataset-Adaptive Dimensionality Reduction](https://mlsanity.com/paper/2507.11984/) (2025)
- [OW-CLIP: Data-Efficient Visual Supervision for Open-World Object Detection via Human-AI Collaboration](https://mlsanity.com/paper/2507.19870/) (2025)
- [Understanding Bias in Perceiving Dimensionality Reduction Projections](https://mlsanity.com/paper/2507.20805/) (2025)
- [UMATO: Bridging Local and Global Structures for Reliable Visual Analytics with Dimensionality Reduction](https://mlsanity.com/paper/2508.16227/) (2025)
- [Assessing the impact of dimensionality reduction on clustering performance - a systematic study](https://mlsanity.com/paper/2604.22099/) (2026)
- [Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack](https://mlsanity.com/paper/2408.17151/) (2024)
- [Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reduction](https://mlsanity.com/paper/2501.10466/) (2025)
- [Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction](https://mlsanity.com/paper/2506.14820/) (2025)
- [Uncertainty-Aware PCA for Arbitrarily Distributed Data Modeled by Gaussian Mixture Models](https://mlsanity.com/paper/2508.13990/) (2025)
- [Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation](https://mlsanity.com/paper/2509.04222/) (2025)
- [A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction](https://mlsanity.com/paper/2604.02535/) (2026)

## Abstract (arXiv)

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the results of a user study that investigates the influence of different DR techniques on visual cluster analysis. Our study focuses on the most concerned property types, namely the linearity and locality, and evaluates twelve representative DR techniques that cover the concerned properties. Four controlled experiments were conducted to evaluate how the DR techniques facilitate the tasks of 1) cluster identification, 2) membership identification, 3) distance comparison, and 4) density comparison, respectively. We also evaluated users' subjective preference of the DR techniques regarding the quality of projected clusters. The results show that: 1) Non-linear and Local techniques are preferred in cluster identification and membership identification; 2) Linear techniques perform better than non-linear techniques in density comparison; 3) UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the best in cluster identification and membership identification; 4) NMF (Nonnegative Matrix Factorization) has competitive performance in distance comparison; 5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has competitive performance in density comparison.