SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 731–740 of 3304 papers

TitleStatusHype
A predictive physics-aware hybrid reduced order model for reacting flows—0
Data Dimension Reduction makes ML Algorithms efficient—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—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
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—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
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—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