Principal Component Analysis

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12 papers in the last 28 days · 0.2% of indexed attention

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High-Dimensional DataDimensionality ReductionIntrinsic DimensionalityLow-Dimensional Structure

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Period ending 2026-09-21

3 new papers

A weekly snapshot of new work published in Principal Component Analysis.

Period ending 2026-09-14

7 new papers

A weekly snapshot of new work published in Principal Component Analysis.

Period ending 2026-09-07

1 new paper

A weekly snapshot of new work published in Principal Component Analysis.

98 papers

Latest in Principal Component Analysis

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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 AnalysisOptimal Sample Complexity

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

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

  3. PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models

    Sep 20, 2026Qi Qin, Erbo Li, Ting Wei +4Tabular LearningPrincipal Component Analysis

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

    Sep 17, 2026Zhenlin Yao, Wei XiongPrincipal Component AnalysisDimensionality Reduction

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

    Sep 14, 2026Kris Lokere, John FossacecaPrincipal Component Analysis

  6. Low-Dimensional Embeddings for Gaussian Kernels on Manifolds

    Sep 14, 2026Soumik Dutta, Kunal DuttaKernel MethodManifolds

  7. 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

  8. Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

    Sep 9, 2026Saurabh Sihag, Andrea Cavallo, Elvin Isufi +2Covariance EstimationNeural Network

  9. Dimensionality Reduction for Hyperspectral Image Classification

    Sep 9, 2026Mohamed Cherifi, Ammar Mesloub, Mohammed Nabil El Korso +2Hyperspectral Image ClassificationMultiple Hyperspectral Benchmarks

  10. A Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning

    Sep 9, 2026Lingxiao Qu, Yan PeiKernel MethodPrincipal Component Analysis

  11. Exact Degeneracy Under Balanced k-Shot Sampling:Consequences for Small-Sample Discriminant Analysis on LLM Embeddings

    Sep 9, 2026Lingxiao QuPrincipal Component AnalysisIndependent Component Analysis

  12. Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships

    Sep 7, 2026Papri DeyMatrix FactorizationBayesian

  13. CLUES-WEASEL: No additional clues required to choose your time series clustering algorithm

    Sep 7, 2026Johann FaouziTime-Series ClassificationImage Clustering

  14. 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 AnalysisPeriodic Domains

  15. Decision Tree and K-Means Analysis of Raman Spectra for Edible Oils: A Physics-Informed AI Approach

    Aug 20, 2026Amrita Shaw, Chandrasekar S. N., Sai Muthukumar V. +2Non-Destructive Frying Oil Oxidation AssessmentRaman

  16. DMDIntel: Interpreting Large Language Models via Dynamic Mode Decomposition

    Aug 13, 2026Amogh Joshi, Animesh Mukherjee, Sergey UtyuzhnikovVisual AttributionNvidia Nemotron

  17. DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces

    Aug 12, 2026Hans Buehler, Blanka Horvath, Anastasis KratsiosDerivative PricingSynthetic Time Series

  18. Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version

    Aug 12, 2026Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur ZimekFeature SelectionUnsupervised

  19. Towards an approach to multivariate outlier detection for District Heating System data

    Aug 11, 2026Rajko Turudija, Dušan Stojiljković, Milan Zdravković +1Hvac ControlOutliers

  20. P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing

    Aug 10, 2026Amoon Jamzad, Dilakshan Srikanthan, Faranak Akbarifar +2Principal Component AnalysisMedical Imaging

  21. A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems

    Aug 9, 2026Sangiya PararajasinghamFacial RecognitionSpoofed Speech

  22. Training-Free Hashing-Based Attention via Binary Principal Components

    Aug 5, 2026Daohai Yu, Zhanpeng Zeng, Keyu Chen +6Dynamic Sparse AttentionQuadratic Attention

  23. SAKI: Score-Aware Low-Rank Key Indexing with Random-Matrix Noise Correction for KV Retrieval

    Aug 4, 2026Lin ZhangKey-Value CacheKey-Value

  24. Detecting high-frequency brain disorder signals using dynamic mode decomposition from EEG

    Aug 3, 2026Jacob Kang, Jong-Hyeon SeoElectroencephalography Spatial Super-ResolutionHigh-Frequency Textures

  25. When Does Deep Representation Learning Help Single-Cell Clustering? A Sensitivity-Aware Diagnostic Benchmark for Biomedical AI Pipelines

    Jul 28, 2026Nguyen Thanh Phong, Truong Viet Vu, Nguyen Ha Thu +4Single-Cell Rna SequencingImage Clustering

  26. Integrating Factual and Normative Industrial Knowledge via Constraint-Aware Graph Attention for Process Plan Recommendation

    Jul 27, 2026Yuntong Chen, Yingqi Li, Yingying Xiao +5Collaborative FilteringPlanning Methods

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

    Jul 25, 2026Baran Koseoglu, Berrin YanikogluFeature SelectionPrincipal Component Analysis

  28. Projection Pursuit CPCANet for Domain Generalization

    Jul 24, 2026Yu-Hsi Chen, Abd-Krim SeghouaneMultimodal Domain GeneralizationDomain Generalization

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

    Jul 23, 2026Sergio A. AlvarezKernel Hilbert SpacesPrincipal Component Analysis

  30. Importance Sampling and PCA for Finding Failures in Commercial Autonomous Vehicles

    Jul 20, 2026Hailey Warner, Duncan Eddy, Shreya Parjan +6Robot FailuresAutonomous Driving

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

    Jul 18, 2026Peng ZhaoPrincipal Component AnalysisKernel Ridge Regression

  32. DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration

    Jul 16, 2026Sara Monji-Azad, Rohit Beer, Marvin Kinz +2Point Cloud RegistrationDeformation

  33. TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance

    Jul 15, 2026Hongye Zeng, Shreeram Athreya, Dingyuan Dai +4PrognosticsLesion Segmentation

  34. ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds

    Jul 13, 2026Ge Zhang Chunyang Wang Bin LiuLight Detection And Ranging Point CloudsVisual Grounding

  35. Bandit PCA with Minimax Optimal Regret

    Jul 12, 2026Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa SahaPrincipal Component AnalysisRegret

  36. Physics-Informed Neural Embeddings of PDE Solution Families

    Jul 7, 2026Raul Jimenez, Svitlana Mayboroda, Pavlos Protopapas +3Partial Differential Equation BenchmarksPhase Space

  37. A study of holes: Topological analysis reveals crowd dynamics regimes in a bidirectional corridor scenario

    Jul 7, 2026Sabrina Desiree Kern, Gerta KösterPersistent HomologyTopological Data Analysis

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

    Jul 6, 2026Jeanie Schreiber, Tyrus Berry, Zeeshan AhmedPrincipal Component AnalysisMasked Autoencoders

  39. Quantum Spectral Anomaly Detection

    Jul 6, 2026Yewei Yuan, Michele Minervini, Mark M. Wilde +1N$-Qubit Stabilizer StatesPrincipal Component Analysis

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

    Jul 6, 2026Ryan Cory-Wright, Jean PauphiletPrincipal Component AnalysisFeature Selection

  41. Simplifying Flow Matching Transformations with Low-Rank Mixture Models

    Jun 29, 2026Liam A. Kruse, Houjun Liu, Alexandros E. Tzikas +2Normalizing FlowsConditional Flow Matching

  42. Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

    Jun 28, 2026Zirui Deng, Jingbo Sun, Deyu Meng +1Neural OperatorsLeast Squares

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

    Jun 20, 2026Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar +1Differential PrivacyPrincipal Component Analysis

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

    Jun 17, 2026Sidharth Brahmandam, Vayd RamkumarQuantum Approximate Optimization AlgorithmVariational Quantum Circuits

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

    Jun 15, 2026Peng Xu, Changbo Zhu, Young-Heon Kim +1Wasserstein DistanceProbability Measures

  46. Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

    Jun 13, 2026Siyu Chen, Beining Wu, Miao Lu +2Optimal Sample ComplexityTwo-Layer Neural Networks

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

    Jun 12, 2026Hamidou TembinePrincipal Component AnalysisLong-Tailed Distribution

  48. Classification of Astronomical Spectra Using PCA-Compressed Flux and Inverse-Variance Features

    Jun 11, 2026Bruno Santos Meneses Barreto, Marcio EisencraftSpectraHyperspectral Image Classification

  49. What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection

    Jun 11, 2026Samuel Webster, Walter ScheirerBiometricsSaliency

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

    Jun 6, 2026Andreas SchlapbachPrincipal Component AnalysisCluster Structure

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

    Jun 6, 2026Alexey Kresin, Tchifou M. Dieffi, Tomer CaspiGender BiasBias Mitigation

  52. Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

    Jun 4, 2026Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2Gaussian ProcessHigh-Dimensional Data

  53. TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning

    Jun 4, 2026Marius Dragoi, Ioana Pintilie, Alexandra Dragomir +2Efficient Continual LearningContinual Learning Methods

  54. Anchor PCA

    Jun 4, 2026Benedikt Seiter, Anya Fries, Julius von Kügelgen +1Principal Component AnalysisDimensionality Reduction

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

    Jun 4, 2026Nelly Elsayed, Zag ElSayed, Navid AsadizanjaniDimensionality ReductionAttack Detection

  56. Towards Pretraining Text Encoders for TabPFN

    Jun 3, 2026Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1Tabular Foundation ModelsTabular Prior-Data Fitted Network

  57. A Robust Optimization Approach to Sparse Principal Component Analysis

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

  58. RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection

    Jun 1, 2026Pingping Liu, Aohua Li, Yubing Lu +3Infrared Small Target DetectionState Space Models

  59. UR-JEPA: Uniform Rectifiability as a Regularizer for Joint-Embedding Predictive Architectures

    May 31, 2026Triet M. LeJoint-Embedding Predictive ArchitecturesSphere-Jepa

  60. Looped Transformers with Layer Normalization Provably Learn the Power Method

    May 30, 2026Lyumin Wu, Chenyang Zhang, Yuan CaoTransformer LayersTransformer Architectures