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 3001–3050 of 3304 papers

TitleStatusHype
Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text Commentary Data—0
Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications—0
Cross-validation of matching correlation analysis by resampling matching weights—0
CSI Compression using Channel Charting—0
CUR Decompositions, Approximations, and Perturbations—0
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning—0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Data Augmentation For Label Enhancement—0
Data Dimension Reduction makes ML Algorithms efficient—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection—0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Data-Enabled Predictive Control for Flexible Spacecraft—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decoding Emotional Experience through Physiological Signal Processing—0
Decoding Imagined Speech and Computer Control using Brain Waves—0
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Compressed Learning for 3D Seismic Inversion—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
Deep Embedding Kernel—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
Deep Features for training Support Vector Machine—0
Deep Gaussian Mixture Models—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Deep-gKnock: nonlinear group-feature selection with deep neural network—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Estimation of a function of low local dimensionality by deep neural networks—0
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures—0
Deep Learning Architecture for Motor Imaged Words—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
Deep Learning for Causal Inference—0
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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