SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2461–2470 of 3304 papers

TitleStatusHype
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality—0
Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension—0
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models—0
Feature Clock: High-Dimensional Effects in Two-Dimensional Plots—0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—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