Dimensionality Reduction

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Overview

Dimensionality reduction turns data with many variables into a smaller representation that is easier to visualize, analyze, store, or use in a predictive model. Linear methods such as PCA and supervised discriminant projections select directions of variation, while t-SNE, UMAP, graph and spectral methods, optimal transport, and autoencoders capture nonlinear structure. Researchers apply these approaches to images, text embeddings, biological data, and dynamical systems, evaluating them through reconstruction, classification, neighborhood, density, and topology preservation. A central tension is that preserving variance or local neighborhoods may discard task-relevant signals or distort global geometry, so methods and evaluations increasingly account for the intended use, uncertainty, and out-of-sample behavior.

Latest papers

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145 papers9 papers in the last four weeks, down 36% on the four weeks before. 0.1% of all new papers.

  1. Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection

    May 9, 2026Lei Wang, Wenxiang Diao, Andrew Busch +2Privacy-Preserving MLVideo Anomaly Detection

    School of Engineering and Built Environment, Griffith University · Data61/CSIRO · School of Computer Science and Engineering, University of New South Wales +1

  2. Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations

    May 5, 2026Seifallah Fetni, Thinh Quy Duc Pham, Truong Vinh Hoang +4Neural Surrogate ModelingAutoencoders

    University of Liège, UEE Research Unit, MSM division, allée de la Découverte, 9 B52/3, B 4000 Liège, Belgium · Chair of Mathematics for Uncertainty Quantification, RWTH-Aachen University, 52056 Aachen, Germany · Institute of Strategy Development, Thu Dau Mot University, 75100 Binh Duong Province, Viet Nam

  3. Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch

    May 5, 2026Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan LuoIntrinsic DimensionalityContrastive Learning

    Northwestern University, IL, USA · University of California

  4. DR-SNE: Density-Regularized Stochastic Neighbor Embedding

    May 3, 2026Maksim KazanskiiDimensionality Reduction

    Independent Researcher

  5. Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization

    May 1, 2026Daniel Hangan, Stephen Kobourov, Jacob MillerScientific VisualizationData Visualization

    Technical University of Munich

  6. Class Angular Distortion Index for Dimensionality Reduction

    May 1, 2026Kaviru Gunaratne, Stephen Kobourov, Jacob MillerData VisualizationDimensionality Reduction

    Technical University of Munich

  7. Deflation-Free Optimal Scoring

    Apr 28, 2026Sharmin Afroz, Brendan AmesOrthogonal OptimizationConstrained Optimization

    Department of Computational & Informational Sciences, Stillman College, Tuscaloosa, AL, USA · School of Mathematical Sciences, University of Southampton, Southampton, UK

  8. DiRe-RAPIDS: Topology-faithful dimensionality reduction at scale

    Apr 28, 2026Alexander Kolpakov, Igor RivinData VisualizationManifold Learning

    CSTEM, University of Austin, Austin, TX, USA · Department of Mathematics, Temple University, Philadelphia, PA, USA

  9. Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding

    Apr 27, 2026Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. EgorovFeature SelectionDimensionality Reduction

    R&D Department T8 LLC Moscow, Russia · Department of Computer Science South-West State University Kursk, Russia

  10. Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering

    Apr 26, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteRepresentation LearningMultidimensional Scaling

    Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Canindé, Ceará, Brazil · Federal University of Ceara, Fortaleza, Ceará, Brazil · Sigma Nova, Paris, France

  11. Assessing the impact of dimensionality reduction on clustering performance - a systematic study

    Apr 23, 2026Ousmane Assani Amate, Elyes Lounissi, Mohammadreza Bakhtyari +3Principal Component AnalysisClustering

    Université du Québec à Montréal, Montreal, Quebec, Canada · Mila - Quebec AI Institute, Montreal, Quebec, Canada

  12. Local Neighborhood Instability in Parametric Projections: Quantitative and Visual Analysis

    Apr 23, 2026Frederik L. Dennig, Daniel A. KeimDistribution Shift RobustnessJacobian Regularization

    University of Konstanz, Germany

  13. Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning

    Apr 20, 2026Alexandre L. M. LevadaPrincipal Component AnalysisManifold Learning

    Computing Department Federal University of São Carlos 13565-905, São Carlos-SP, Brazil

  14. Neighbor Embedding for High-Dimensional Sparse Poisson Data

    Apr 18, 2026Noga Mudrik, Adam S. CharlesManifold LearningDimensionality Reduction

    Department of Biomedical Engineering Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University Baltimore, MD 21218, USA

  15. Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization

    Apr 15, 2026Sida Liu, Yangzi Guo, Mingyuan WangUnsupervised ClusteringDimensionality Reduction

    Department of Statistics, Florida State University, Tallahassee, Florida, USA · Department of Mathematics, Florida State University, Tallahassee, Florida, USA

  16. Some Theoretical Limitations of t-SNE

    Apr 14, 2026Rupert Li, Elchanan MosselDimensionality ReductionData Visualization

  17. Does Dimensionality Reduction via Random Projections Preserve Landscape Features?

    Apr 14, 2026Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1Black-Box OptimizationRandomized Sketching

    Leiden Institute of Advanced Computer Science, Leiden University · Leiden, The Netherlands · RTWH Aachen University +1

  18. On the continuum limit of t-SNE for data visualization

    Apr 13, 2026Jeff Calder, Zhonggan Huang, Ryan Murray +1Data VisualizationDimensionality Reduction

    School of Mathematics, University of Minnesota · Department of Mathematics, University of Utah · Department of Mathematics, North Carolina State University

  19. Classification of Epileptic iEEG using Topological Machine Learning

    Apr 13, 2026Sunia Tanweer, Narayan Puthanmadam Subramaniyam, Firas A. KhasawnehEEG Seizure DetectionTopological Data Analysis

    Department of Mechanical Engineering, Michigan State University, East Lansing, 48823, MI, USA. · Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, 48823, MI, USA. · Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. +1

  20. Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models

    Apr 11, 2026Yu Jiang, Hanwen Jiang, Ahmed Abdelkader +4Parameter-Efficient Fine-TuningLow-Rank Adaptation

    The University of Texas at Austin · Shanghai Jiao Tong University · Adobe Research +1

  21. Bi-Lipschitz Autoencoder With Injectivity Guarantee

    Apr 8, 2026Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +2AutoencodersRepresentation Learning

    University of Pennsylvania

  22. Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look

    Apr 5, 2026Indar Kumar, Girish Karhana, Sai Krishna Jasti +1Dimensionality ReductionTransfer Learning

    independent researchers

  23. Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport

    Apr 3, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteMultidimensional ScalingMulti-View Learning

    Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Canindé, Ceará, Brazil · Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil · Sigma Nova, Paris, France

  24. A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction

    Apr 2, 2026Zeyang Huang, Angelos Chatzimparmpas, Thomas Höllt +1Dimensionality ReductionSpectral Methods

    Linköping University, Sweden · Utrecht University, The Netherlands · University of Bergen, Norway +1

  25. The Rashomon Effect for Visualizing High-Dimensional Data

    Apr 1, 2026Yiyang Sun, Haiyang Huang, Gaurav Rajesh Parikh +1Dimensionality Reduction

    Duke University

  26. Nonlinear Factor Decomposition via Kolmogorov-Arnold Networks: A Spectral Approach to Asset Return Analysis

    Mar 30, 2026David BreazuDimensionality ReductionPrincipal Component Analysis

    Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania

  27. Diffusion Maps is not Dimensionality Reduction

    Mar 30, 2026Julio Candanedo, Alejandro PatiñoManifold LearningDimensionality Reduction

    SparseTrace.ai. Appleton, WI, USA. · Universidad Nacional de Colombia sede Manizales. Manizales, Caldas, Colombia

  28. Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots

    Mar 24, 2026Paul Leonard Wolff, Hugo Buurmeijer, Luis Pabon +6Continuum Robot ControlRobot Dynamics Learning

    Autonomous Systems Lab, Stanford University, USA. · Soft Robotics Lab, ETH Zurich, Switzerland. · Institute of Mechanical Systems, ETH Zurich, Switzerland. +1

  29. Spectral Tempering for Embedding Compression in Dense Passage Retrieval

    Mar 19, 2026Yongkang Li, Panagiotis Eustratiadis, Evangelos KanoulasEmbedding CompressionDimensionality Reduction

    University of Amsterdam, Amsterdam, The Netherlands

  30. Manifold-Matching Autoencoders

    Mar 17, 2026Laurent Cheret, Vincent Létourneau, Isar Nejadgholi +3Multidimensional ScalingAutoencoders

    Department of Computer Science, University of Ottawa, Ottawa, Canada · MILA, Montréal, Canada · National Research Council of Canada, Ottawa, Canada