Principal Component Analysis

Also known as PCA

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7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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  1. Gap-Free Streaming PCA Beyond Rank-One Updates: Near-Optimal Rates and Applications to Differential Privacy

    Sep 22, 2026Anming Gu, Syamantak Kumar, Kevin Tian +1Principal Component AnalysisDifferential Privacy

    University of Texas at Austin

  2. SuperPCA: subspace analysis and an efficient algorithm for high-dimensional PCA

    Sep 22, 2026Irina-Beatrice Haas, Maike Meier, Yuji Nakatsukasa +1Principal Component AnalysisDimensionality Reduction

    Mathematical Institute, University of Oxford, Oxford, OX2 6GG, UK · Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, 9700 AB, Groningen, The Netherlands · Institute of Mathematics, EPFL, 1015 Lausanne, Switzerland

  3. Identifying Representational Biases in Datasets Using PCA: A Max-Disparity Partition Framework

    Sep 21, 2026Arjun KM, Shashi JainPrincipal Component AnalysisAlgorithmic Fairness

    Department of Management Studies Indian Institute of Science, Bangalore

  4. Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs

    Sep 17, 2026Zhenlin Yao, Wei XiongPrincipal Component AnalysisRandom Feature Methods

    School of Statistics University of International Business and Economics Beijing, China

  5. Rotation-Based Subspace Tracking for Robust Kernel PCA on Streaming Data

    Sep 14, 2026Kris Lokere, John FossacecaPrincipal Component AnalysisStreaming Algorithms

    Harvard University, Cambridge, MA, USA · George Washington University, Washington, DC, USA

  6. A Functional SVD Framework for Regularized Multivariate Functional PCA with Dual Penalization

    Sep 13, 2026Yue Zhao, Hossein Haghbin, Rebecca Sanders +1Principal Component AnalysisSingular Value Decomposition

    Division of Biostatistics and Health Data Science, University of Minnesota, USA · Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Iran · Department of Mathematical and Statistical Sciences, Marquette University, USA

  7. Two-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning

    Sep 7, 2026Mrigank Dhingra, Jordan Stout, Omer SanPrincipal Component AnalysisReduced-Order Modeling

    Department of Mechanical & Aerospace Engineering University of Tennessee Knoxville, Tennessee 37917, USA · Department of Mathematics and Statistics Boston University Boston, Massachusetts 02215, USA

  8. Calendar-Structured Sparse Principal Component Analysis for Interpretable Multi-Periodic Electricity Consumption Profiles

    Sep 5, 2026Carlos Quesada-Granja, Tony Castillo-Calzadilla, Carlos Rizo-MaestrePrincipal Component AnalysisStructured Sparsity

    Dept. of Computer Science and Artificial Intelligence, University of Alicante. Ctra. de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain. · Dept. of Industrial Engineering, University of La Laguna. Camino San Francisco de Paula s/n, 38200 San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain. · Dept. of Architectural Constructions, University of Alicante. Ctra. de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain.

  9. Variance-Preserving Orthogonal Selection (VPOS): Greedy Feature Selection via Orthogonal Deflation in PCA Loading Space

    Jul 25, 2026Baran Koseoglu, Berrin YanikogluPrincipal Component AnalysisFeature Selection

    Independent Researcher, London, UK · Sabanci University, Istanbul, Turkey

  10. Projection Pursuit CPCANet for Domain Generalization

    Jul 24, 2026Yu-Hsi Chen, Abd-Krim SeghouanePrincipal Component AnalysisDomain Generalization

    The University of Melbourne

  11. Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA

    Jul 23, 2026Sergio A. AlvarezPrincipal Component AnalysisReproducing Kernel Hilbert Spaces

    Department of Computer Science, Boston College, Chestnut Hill, MA 02467 USA

  12. De-floored Principal Component Regression: When Rank Selection Alone Is Insufficient for Prediction

    Jul 18, 2026Peng ZhaoPrincipal Component AnalysisLinear Regression

    Department of Applied Economics and Statistics, University of Delaware

  13. Bandit PCA with Minimax Optimal Regret

    Jul 12, 2026Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa SahaPrincipal Component AnalysisMinimax Regret

    Georgia Tech · University of Illinois, Chicago

  14. Orthogonal Dendritic Intrinsic Networks: An Architecture for Significance-Ordered, Orthogonal Latent Spaces

    Jul 6, 2026Jeanie Schreiber, Tyrus Berry, Zeeshan AhmedRepresentation LearningPrincipal Component Analysis

    Department of Mathematical Sciences George Mason University Fairfax, VA 22030, USA · Sensor Science Division, Physical Measurement Laboratory NIST Gaithersburg, MD 20899, USA

  15. msPCA: An R Package for Sparse PCA with Multiple Components

    Jul 6, 2026Ryan Cory-Wright, Jean PauphiletPrincipal Component AnalysisDimensionality Reduction

    Analytics, Marketing and Operations Imperial Business School · Management Science and Operations London Business School

  16. On the Curse of Dimensionality in Private Sparse Covariance Estimation and PCA

    Jun 20, 2026Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar +1Principal Component AnalysisMinimax Estimation

    University of Texas at Austin

  17. Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut

    Jun 17, 2026Sidharth Brahmandam, Vayd RamkumarPrincipal Component AnalysisCombinatorial Optimization

    Naperville North High School · Independent Researcher

  18. Another Look at Log-PCA for Probability Measures: A Dynamical Formulation and Statistical Convergence

    Jun 15, 2026Peng Xu, Changbo Zhu, Young-Heon Kim +1Principal Component AnalysisStatistical Learning Theory

    Department of Statistics University of Illinois Urbana-Champaign · Department of ACMS University of Notre Dame · Department of Mathematics University of British Columbia +1

  19. The Risk Shadow of Principal Component Analysis: When 99.9999% Variance Preservation Causes Catastrophic Decision Errors

    Jun 12, 2026Hamidou TembinePrincipal Component AnalysisDimensionality Reduction

    Department of EECS, School of Engineering, UQTR, Canada. Learning and Game Theory Laboratory (LnG Lab), TIMADIE.

  20. Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA

    Jun 6, 2026Andreas SchlapbachIntrinsic DimensionalityPrincipal Component Analysis

    Swiss Federal Railways (SBB), Switzerland

  21. What Does Debiasing Really Remove? A Geometric Study of PCA-Based Gender Debiasing in Word Embeddings

    Jun 6, 2026Alexey Kresin, Tchifou M. Dieffi, Tomer CaspiNeural Representation GeometryWord Embeddings

    Department of Computer Science, Hood College, Frederick, MD, United States · Department of Mathematics, Ben-Gurion University of the Negev, Be'er Sheva, Israel

  22. Anchor PCA

    Jun 4, 2026Benedikt Seiter, Anya Fries, Julius von Kügelgen +1Unsupervised LearningPrincipal Component Analysis

    ETH Zürich

  23. Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding

    Jun 4, 2026Nelly Elsayed, Zag ElSayed, Navid AsadizanjaniPredictive CodingPrincipal Component Analysis

    School of Information Technology University of Cincinnati Ohio, United States · Dept. of Electrical & Computer Engineering University of Florida Florida, United States

  24. A Robust Optimization Approach to Sparse Principal Component Analysis

    Jun 2, 2026David Vävinggren, Francis Bach, André M. H. Teixeira +2Principal Component AnalysisRobust Optimization

    Uppsala University, Sweden · PSL Research University / INRIA, France · Science for Life Laboratory, Sweden

  25. Easy, robust approximate message passing for planted spike models

    May 30, 2026Misha Ivkov, Tselil SchrammRandom Matrix TheoryPrincipal Component Analysis

    *Stanford University. · †Stanford University.

  26. Quantum principal component analysis without eigenvector recovery

    May 27, 2026Yewei Yuan, Michele Minervini, Mark M. Wilde +1Principal Component AnalysisQuantum Machine Learning

    Global College, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14850, United States · Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China +1

  27. Metric-Aware PCA as a Linear Instance of Geometric Deep Learning

    May 25, 2026Michael LeznikPrincipal Component AnalysisGeometric Representation Learning

  28. Mean-Shift PCA by Knockoff Mean

    May 25, 2026Mengda Li, Zeng Li, Jianfeng YaoDistribution Shift RobustnessPrincipal Component Analysis

    School of Data Science, The Chinese University of Hong Kong, Shenzhen, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

  29. Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis

    May 20, 2026Kisung YouPrincipal Component Analysis

    Department of Mathematics, Baruch College · Department of Mathematics, The Graduate Center, City University of New York

  30. Beyond Explained Variance: A Cautionary Tale of PCA

    May 13, 2026Gionni MarchettiData VisualizationPrincipal Component Analysis

    Barcelona, Spain