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A survey of dimensionality reduction techniques

arXiv 1403.2877 ↗First posted 12 Mar 2014stat.ML cs.LG q-bio.QMcited by 50

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Abstract (arXiv)

Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks when dealing with such high dimensional data. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information. The mathematical procedures making possible this reduction are called dimensionality reduction techniques; they have widely been developed by fields like Statistics or Machine Learning, and are currently a hot research topic. In this review we categorize the plethora of dimension reduction techniques available and give the mathematical insight behind them.

Key facts. Cited by 50 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Mar 2014.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 11; 2024: 9; 2025: 8; 2026: 4.

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  1. 2024At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligencecited by 25
  2. 2023FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systemscited by 24
  3. 2022A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problemscited by 20
  4. 2023To Transformers and Beyond: Large Language Models for the Genomecited by 15
  5. 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainabilitycited by 12
  6. 2023Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Featurescited by 10
  7. 2021UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Datacited by 8
  8. 2025Stop Misusing t-SNE and UMAP for Visual Analyticscited by 7
  9. 2021LossPlot: A Better Way to Visualize Loss Landscapescited by 5
  10. 2023Enhancing Neural Training via a Correlated Dynamics Modelcited by 5
  11. 2024"Normalized Stress" is Not Normalized: How to Interpret Stress Correctlycited by 5
  12. 2021HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameterscited by 4
38 more
  1. 2022Local Explanation of Dimensionality Reductioncited by 4
  2. 2022Feature-Based Time-Series Analysis in R using the theft Packagecited by 4
  3. 2023ShaRP: Shape-Regularized Multidimensional Projectionscited by 4
  4. 2025Creating User-steerable Projections with Interactive Semantic Mappingcited by 3
  5. 2021Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectivecited by 2
  6. 2021Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reductioncited by 2
  7. 2023Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysiscited by 2
  8. 2023Nonlinear Feature Aggregation: Two Algorithms driven by Theorycited by 2
  9. 2025Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practicescited by 2
  10. 2021Inspecting the Process of Bank Credit Rating via Visual Analyticscited by 1
  11. 2022LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classificationcited by 1
  12. 2023Toward Flare-Free Images: A Surveycited by 1
  13. 2024Predicting loss-of-function impact of genetic mutations: a machine learning approachcited by 1
  14. 2024Circuit design in biology and machine learning. I. Random networks and dimensional reductioncited by 1
  15. 2025Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysicscited by 1
  16. 2025Supervised Dynamic Dimension Reduction with Deep Neural Networkcited by 1
  17. 2025How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metricscited by 1
  18. 2021Multidimensional Scaling for Gene Sequence Data with Autoencoderscited by 0
  19. 2021Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Setscited by 0
  20. 2021Non-Parametric Manifold Learningcited by 0
  21. 2021An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representationcited by 0
  22. 2022MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problemscited by 0
  23. 2022Jazz Contrafact Detectioncited by 0
  24. 2022A GA-like Dynamic Probability Method With Mutual Information for Feature Selectioncited by 0
  25. 2022A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniquescited by 0
  26. 2023Application of probabilistic modeling and automated machine learning framework for high-dimensional stress fieldcited by 0
  27. 2023Metastatic Breast Cancer Prognostication Through Multimodal Integration of Dimensionality Reduction Algorithms and Classification Algorithmscited by 0
  28. 2023Using Slisemap to interpret physical datacited by 0
  29. 2024Evaluating the Stability of Deep Learning Latent Feature Spacescited by 0
  30. 2024Formation-Controlled Dimensionality Reductioncited by 0
  31. 2024Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint trackingcited by 0
  32. 2024SCADE: Scalable Framework for Anomaly Detection in High-Performance Systemcited by 0
  33. 2025Manifold Learning for Hyperspectral Imagescited by 0
  34. 2025From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cupcited by 0
  35. 2026LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Datacited by 0
  36. 2026Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Lookcited by 0
  37. 2026LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Modelscited by 0
  38. 2026CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Datacited by 0