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 2311–2320 of 3304 papers

TitleStatusHype
T-SNE Is Not Optimized to Reveal Clusters in Data—0
Tuning-Free Disentanglement via Projection—0
Two Approaches to Supervised Image Segmentation—0
Two-sample test based on Self-Organizing Maps—0
Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging—0
UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes—0
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection—0
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data—0
Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA—0
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization—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