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 631–640 of 3304 papers

TitleStatusHype
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language ModelsCode1
Agnostically Learning Multi-index Models with Queries—0
LeanVec: Searching vectors faster by making them fitCode2
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks—0
Nowcasting Madagascar's real GDP using machine learning algorithms—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—0
Deep Learning for Efficient GWAS Feature Selection—0
Augment on Manifold: Mixup Regularization with UMAP—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Show:102550
← PrevPage 64 of 331Next →
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