Dimensionality Reduction

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4 papers in the last 28 days · 0.1% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

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Period ending 2026-09-21

2 new papers

A weekly snapshot of new work published in Dimensionality Reduction.

Period ending 2026-09-14

1 new paper

A weekly snapshot of new work published in Dimensionality Reduction.

52 papers

Latest in Dimensionality Reduction

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Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs
Sep 17, 2026Zhenlin Yao, Wei XiongPrincipal Component Analysis Dimensionality Reduction
Generating the Unheard: Phylogeny-Guided Latent Generation for Ancestral Sound Reconstruction
Sep 14, 2026Tianyi Xu, Shrinaath Narasimhan, Evan Gorstein +3Acoustic Representation Phylogenetic Trees
Dimensionality Reduction for Hyperspectral Image Classification
Sep 9, 2026Mohamed Cherifi, Ammar Mesloub, Mohammed Nabil El Korso +2Hyperspectral Image Classification Multiple Hyperspectral Benchmarks
Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
Aug 30, 2026Louen Pottier, Louis Lesueur, Anders ThorinMasked Autoencoders Dimensionality Reduction
CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data
Aug 11, 2026Fenosoa Randrianjatovo, Maya Saleh, Simon Girard +1Dimensionality Reduction Single-Cell Rna Sequencing
A Convolutional Layer Activation Dimensionality Reduction for Out-of-Distribution and Adversarial Attack Detection Methods
Aug 10, 2026Leandro de Souza Rosa, Lorenzo Capelli, Clara Nunes Barrancos +2Out-Of-Distribution Detection Convolutional
Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds
Aug 5, 2026Yizhu Wang, Mu Niu, Xiaochen YangIdentity-Conditioned Latent Diffusion Models Latent Diffusion Model
Measuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index
Jul 30, 2026Jaume Ros, Alessio Arleo, Fernando PaulovichDimensionality Reduction Distortion
FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction
Jul 29, 2026Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva +1Dimensionality Reduction Feature Importance
FloDR: An invertible dimensionality reduction method based on a normalising flow
Jul 28, 2026Abdallah Baraka, Daniel ProbstDimensionality Reduction Embedding Dimension
Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}
Jul 22, 2026Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis +1Nonnegative Matrix Factorization Matrix Factorization
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Jul 21, 2026Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1Gaussian Process Dimensionality Reduction
How to Tame Grokking: Representation Geometry as a Control Signal
Jul 13, 2026Maksim A KazanskiiGrokking Multilayer Perceptrons
Mathematics of Data Science
Jul 11, 2026Afonso S. Bandeira, Amit Singer, Thomas StrohmerDimensionality Reduction Graph Laplacians
Terminal Dimension Reduction for Time Series with Applications
Jul 10, 2026Alexander Munteanu, Matteo Russo, David Saulpic +1Dimensionality Reduction Time Series
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
Jul 9, 2026Duen Horng Chau, Donghao Ren, Fred Hohman +1Dimensionality Reduction High-Dimensional Data
Dimension Reduction for Curves: Simplified and Generalized
Jul 3, 2026Matthijs Ebbens, Jie Lu, Alexander MunteanuDimensionality Reduction Curves
Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
Jun 26, 2026Joyanta Jyoti Mondal, Ibne Farabi Shihab, Anuj SharmaLinear Contextual Bandits Multi-Armed Bandits
Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut
Jun 17, 2026Sidharth Brahmandam, Vayd RamkumarQuantum Approximate Optimization Algorithm Variational Quantum Circuits
What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection
Jun 11, 2026Samuel Webster, Walter ScheirerBiometrics Saliency
Bridging Multi-Valued Heuristics and Dimensionality Reduction in Multi-Objective Search
Jun 5, 2026Maya Wolff, Ariel Felner, Oren SalzmanShortest Paths Heuristics
Anchor PCA
Jun 4, 2026Benedikt Seiter, Anya Fries, Julius von Kügelgen +1Principal Component Analysis Dimensionality Reduction
Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding
Jun 4, 2026Nelly Elsayed, Zag ElSayed, Navid AsadizanjaniDimensionality Reduction Attack Detection
When Is 0.1% Enough? Analyzing the Combined Effects of Dimensionality Reduction and Quantization on Text Embedding Compression
May 31, 2026Riku Kisako, Hayato Tsukagoshi, Ryohei SasanoLarge Language Model Compression Domain-Invariant Text Embeddings
FiSeR: Fine-Grained Source Representations for Cross-Domain AI Image Detection
May 30, 2026Shan Zhang, Yongxin He, Mingming Zhang +2Synthetic Image Detection Contrastive Learning
Riemannian Stochastic Optimization for Sufficient Dimension Reduction
May 29, 2026Thibault Pautrel, François PortierDimensionality Reduction Riemannian Optimization
ScaleMAP: Preserving Local Density and Neighborhood Structure in Low-Dimensional Embeddings
May 28, 2026Rajas Poorna, Marcus T. CiceroneDimensionality Reduction Neighbors
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
May 28, 2026Yueyang Wang, Xili Wang, Kejun Tang +3Inverse Problem Dimensionality Reduction
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
May 27, 2026Shiyuan Zuo, Jiashuo Li, Rongfei Fan +2Federated Learning Dimensionality Reduction
Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks
May 26, 2026Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan +2Dimensionality Reduction Shallow Water Equations
Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
May 26, 2026Sai-Aakash Ramesh, Archit Sood, Andrew Corbett +1Dimensionality Reduction Distributional Learning
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Kernel Method Dimensionality Reduction
MEDAL: Manifold Embedding Distillation via Autoencoder Learning
May 22, 2026Irene Chang, Tarek M. Zikry, Genevera I. AllenDimensionality Reduction Manifolds
When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
May 22, 2026Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. PaulovichDimensionality Reduction Distortion
DIVE: Embedding Compression via Self-Limiting Gradient Updates
May 20, 2026Dongfang ZhaoContext Compression Dimensionality Reduction
NOFE - Neural Operator Function Embedding
May 12, 2026Lars Uebbing, Harald L. Joakimsen, Siyan Chen +6Dimensionality Reduction Fourier Neural Operators
A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
May 12, 2026Andrea Serani, Giorgio Palma, Matteo DiezDimensionality Reduction Superquadrics
FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling
May 12, 2026Hongmin LiDimensionality Reduction Statistical Manifold
DR-SNE: Density-Regularized Stochastic Neighbor Embedding
May 3, 2026Maksim KazanskiiDimensionality Reduction Density Ratio Estimation
Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
May 1, 2026Daniel Hangan, Stephen Kobourov, Jacob MillerDimensionality Reduction Stochastic Gradient Descent
Class Angular Distortion Index for Dimensionality Reduction
May 1, 2026Kaviru Gunaratne, Stephen Kobourov, Jacob MillerDimensionality Reduction Distance
DiRe-RAPIDS: Topology-faithful dimensionality reduction at scale
Apr 28, 2026Alexander Kolpakov, Igor RivinDimensionality Reduction Topological Data Analysis
Assessing the impact of dimensionality reduction on clustering performance -- a systematic study
Apr 23, 2026Ousmane Assani-Amate, Mohammadreza Bakhtyari, Émilie Roy +1Dimensionality Reduction Image Clustering
Causal Disentanglement-Inspired Degradation Representation Learning for Full-Reference Image Quality Assessment
Apr 23, 2026Zhen Zhang, Jielei Chu, Tian Zhang +5Image Quality Assessment Representation Learning
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 Equations Jacobian
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Apr 14, 2026Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1Dimensionality Reduction Optimization Landscape
A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction
Apr 2, 2026Zeyang Huang, Angelos Chatzimparmpas, Thomas Höllt +1Dimensionality Reduction Multiscale
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Mar 13, 2026Nicolò Botteghi, Silke Glas, Christoph BruneReduced-Order Models Masked Autoencoders
Beyond Correlation: Learning Supervised, Sample-Distinct, and Eigenimage-Interpretable Representations
Jul 22, 2025Mojtaba MoattariDimensionality Reduction Face Embeddings
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
Jan 31, 2025Daniel Herrera-Esposito, Johannes BurgeDimensionality Reduction Fisher Information
Methods to integrate multinormals and compute classification measures
Dec 23, 2020Abhranil Das, Wilson S GeislerDimensionality Reduction Camouflage
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
Date pendingLuo Long, Coralia Cartis, Paz Fink ShustinBayesian Optimization Dimensionality Reduction

Sep 17, 2026 · stat.MLJ/K move · Enter open · S save

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

Zhenlin Yao, Wei Xiong

Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation. We investigate these distinctions for Online Kernel Supervised Principal Component Analysis (OKSPCA), which combines a centered cross-moment in finite random-feature coordinates with an Adam-style orthonormal basis update for an established objective. Fixed-map consistency, concentration and perturbation results describe the estimator and its exact subspace; same-target comparisons then assess the practical iterate separately. Across six predictive benchmarks, performance depends on the declared pipeline: replacing the tracker with the exact empirical target leaves the two regression deficits largely unchanged. Direct classification-rank models capture nearly all terminal objective energy on average, but a saved intermediate state exhibits substantial geometric deviation; a controlled sample-size study further separates empirical accuracy from population recovery. In distinct numerical-service workloads, exact on-request computation is faster in the tested classification settings, whereas Adam saves time relative to the tested full thin-SVD service for some dense wider-regression requests, alongside persistent geometric error. These diagnostics limit explanations based solely on terminal optimization accuracy and distinguish numerical cost from quality, rank coverage and freshness; they establish neither practical-tracker convergence nor predictive or deployment benefits from basis availability.

Principal Component Analysis Dimensionality Reduction Adam Self-Supervised Trade-Off

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