SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 431–440 of 3304 papers

TitleStatusHype
A t-SNE Based Classification Approach to Compositional Microbiome Data—0
Attention-based Supply-Demand Prediction for Autonomous Vehicles—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Attention or memory? Neurointerpretable agents in space and time—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—0
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre—0
A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition—0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals—0
Active Linear Regression for _p Norms and Beyond—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—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