SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

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Showing 1–50 of 3304 papers

TitleStatusHype
Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
Active Learning for Manifold Gaussian Process RegressionCode0
Distributed Lyapunov Functions for Nonlinear NetworksCode0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—0
Local Averaging Accurately Distills Manifold Structure From Noisy Data—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques—0
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-ResolutionCode0
Let the Tree Decide: FABART A Non-Parametric Factor Model—0
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions—0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—0
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction—0
Enabling stratified sampling in high dimensions via nonlinear dimensionality reductionCode0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales—0
Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reductionCode0
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response—0
Quantum Cognition Machine Learning for Forecasting Chromosomal Instability—0
Learning Treatment Representations for Downstream Instrumental Variable Regression—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
A DNA Methylation Classification Model Predicts Organ and Disease Site—0
Riemannian Principal Component Analysis—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—0
Localizing Persona Representations in LLMs—0
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement LearningCode0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis—0
ALPCAHUS: Subspace Clustering for Heteroscedastic DataCode0
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems—0
Exponential Convergence of CAVI for Bayesian PCA—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
NOMAD Projection—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
InstanceBEV: Unifying Instance and BEV Representation for Global Modeling—0
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal EmbeddingsCode0
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
Threshold Strategy for Leaking Corner-Free Hamilton-Jacobi Reachability with Decomposed Computations—0
VizCV: AI-assisted visualization of researchers' publications tracks—0
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian GeometryCode0
ALPCAH: Subspace Learning for Sample-wise Heteroscedastic DataCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified