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 1971–1980 of 3304 papers

TitleStatusHype
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery—0
Robust Support Vector Machines for Speaker Verification Task—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Robust Unsupervised Fault Diagnosis For High-Dimensional Nonlinear Noisy Data—0
RRScell method for automated single-cell profiling of multiplexed immunofluorescence cancer tissue—0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
Run-to-Run Control With Bayesian Optimization for Soft Landing of Short-Stroke Reluctance Actuators—0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds—0
Sample complexity and effective dimension for regression on manifolds—0
Show:102550
← PrevPage 198 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