Principal Component Analysis

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Period ending 2026-09-21

8 new papers

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

Period ending 2026-09-14

8 new papers

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

Period ending 2026-09-07

4 new papers

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279 papers

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  1. Rethinking Intrinsic Dimension Estimation in Neural Representations

    Apr 22, 2026Rickmer Schulte, David RügamerImplicit Neural RepresentationsIntrinsic Dimensionality

  2. uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN

    Apr 22, 2026Sha Lu, Jixue Liu, Stefan Peters +4Reconstruction-Based Anomaly DetectionAnomaly Detection

  3. Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systems

    Apr 21, 2026Huy Hoang Le, Haoguang Wang, Christian Moya +2Ordinary Differential EquationsJacobian

  4. Fast estimation of Gaussian mixture components via centering and singular value thresholding

    Apr 21, 2026Huan QingGaussian Mixture ModelsHigh-Dimensional Data

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

    Apr 20, 2026Alexandre L. M. LevadaManifold Learning MethodPrincipal Component Analysis

  6. The Virtue of Sparsity in Complexity

    Apr 18, 2026Nima Afsharhajari, Jonathan Yu-Meng LiPortfolio OptimizationSparsity

  7. A proposal for PU classification under Non-SCAR using clustering and logistic model

    Apr 18, 2026Konrad Furmanczyk, Kacper PaczutkowskiImage ClusteringPositive--Unlabeled Learning

  8. PRIM-cipal components analysis

    Apr 16, 2026Tianhao Liu, Daniel Andrés Díaz-Pachón, J. Sunil RaoPrincipal Component Analysis

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

    Apr 14, 2026Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1Dimensionality ReductionOptimization Landscape

  10. A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction

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

  11. Finding Low Star Discrepancy 3D Kronecker Point Sets Using Algorithm Configuration Techniques

    Apr 1, 2026Imène Ait Abderrahim, Carola Doerr, Martin DurandMaximum Mean DiscrepancyInformation-Geometric Optimization

  12. i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data

    Mar 25, 2026Chen Ma, Wanjie Wang, Shuhao FanFeature SelectionUnsupervised

  13. Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data

    Mar 24, 2026Anand Jerry George, Nicolas MacrisScore-Based Diffusion ModelLow-Dimensional Structure

  14. Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks

    Mar 16, 2026Yuri Kinoshita, Naoki Nishikawa, Taro ToyoizumiLow-Dimensional StructureContext Compression

  15. Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems

    Mar 13, 2026Nicolò Botteghi, Silke Glas, Christoph BruneReduced-Order ModelsMasked Autoencoders

  16. Spatial Transcriptomics as Images for Large-Scale Pretraining

    Mar 13, 2026Yishun Zhu, Jiaxin Qi, Jian Wang +2Spatial TranscriptomicsSpatial Patching

  17. Manifold-Aligned Generative Transport

    Feb 23, 2026Xinyu Tian, Xiaotong ShenOptimal TransportLow-Dimensional Structure

  18. Least but not Last: Fine-tuning Intermediate Principal Components for Better Performance-Forgetting Trade-Offs

    Feb 3, 2026Alessio Quercia, Arya Bangun, Ira Assent +1Low-Rank AdaptationLearning Rates

  19. PCA of probability measures: Sparse and Dense sampling regimes

    Feb 2, 2026Gachon Erell, Jérémie Bigot, Elsa CazellesPrincipal Component AnalysisProbability Measures

  20. Graph Regularized PCA

    Jan 15, 2026Antonio Briola, Marwin Schmidt, Fabio Caccioli +4Principal Component AnalysisGraph Representation Learning

  21. Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation

    Jan 6, 2026Jarek DudaRotation-Invariant DescriptorsPrincipal Component Analysis

  22. Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Numerical Optimization

    Nov 11, 2025Julian G. SoltesHigh-Dimensional DataMean Squared Error

  23. Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization

    Oct 2, 2025Dhruv Kohli, Sawyer J. Robertson, Gal Mishne +1Tangent SpaceProximal Gradient Descent

  24. Panorama: Fast-Track Nearest Neighbors

    Oct 1, 2025Vansh Ramani, Alexis Schlomer, Akash Nayar +3Approximate Nearest-Neighbor SearchEmbedding Dimension

  25. Beyond Correlation: Learning Supervised, Sample-Distinct, and Eigenimage-Interpretable Representations

    Jul 22, 2025Mojtaba MoattariDimensionality ReductionFace Embeddings

  26. Understanding Two-Layer Neural Networks with Smooth Activation Functions

    Jul 11, 2025Changcun HuangTwo-Layer Neural NetworksActivation Functions

  27. Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models

    Jun 16, 2025Zhenyu Liao, Michael W. MahoneyHigh-Dimensional DataLargest Eigenvalue

  28. Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation

    May 21, 2025Kun Fang, Qinghua Tao, Mingzhen He +6Out-Of-Distribution DetectionIn-Distribution

  29. Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction

    May 10, 2025Michael W. Trosset, Kaiyi Tan, Minh Tang +1Principal Component AnalysisKernel Method

  30. Localized Diffusion Models

    May 7, 2025Georg A. Gottwald, Shuigen Liu, Youssef Marzouk +2Score-Based Diffusion ModelLow-Dimensional Structure

  31. Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction

    Jan 31, 2025Daniel Herrera-Esposito, Johannes BurgeDimensionality ReductionFisher Information

  32. Low-dimensional adaptation of diffusion models: Convergence in total variation

    Jan 22, 2025Jiadong Liang, Zhihan Huang, Yuxin ChenScore-Based Diffusion ModelDenoising Diffusion Probabilistic Model

  33. SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence

    Dec 6, 2024Bowei Tian, Yexiao He, Ziyao Wang +3Fairness-Aware LearningCounterfactual Learning

  34. Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions

    Sep 4, 2024Peng Wang, Huijie Zhang, Zekai Zhang +3Low-Dimensional StructureDistributional Learning

  35. Brownian Motion with a Pulse: A Biostatistician's Guide to Diffusions, Bridges, Functional PCA, and First-Passage Models

    Aug 16, 2024Eliuvish Han CuiStochastic Differential EquationsTime-To-Event Data

  36. Anisotropic View Distance Metric for High-Dimensional Data: Theory, Geometry, and Fast Computation

    Jun 10, 2022Yiqun Zhang, Hou-biao LiK-MeansDistance

  37. Methods to integrate multinormals and compute classification measures

    Dec 23, 2020Abhranil Das, Wilson S GeislerDimensionality ReductionCamouflage

  38. Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization

    Date pendingLuo Long, Coralia Cartis, Paz Fink ShustinBayesian OptimizationDimensionality Reduction

  39. Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics

    Date pendingAnima Kujur, Zahra MonfaredNeural RecordingsPrincipal Component Analysis