SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1251–1275 of 3304 papers

TitleStatusHype
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
Data-Enabled Predictive Control for Flexible Spacecraft—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
A Process for Topic Modelling Via Word Embeddings—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
A Probabilistic Graph Coupling View of Dimension Reduction—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model—0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction—0
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Data Dimension Reduction makes ML Algorithms efficient—0
A predictive physics-aware hybrid reduced order model for reacting flows—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