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 3161–3170 of 3304 papers

TitleStatusHype
Dimensionality Reduction Using the Sparse Linear Model—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
Dimensionality reduction via path integration for computing mRNA distributions—0
Dimensionality Reduction via Regression in Hyperspectral Imagery—0
Dimensionality reduction with missing values imputation—0
Dimensionality reduction with subgaussian matrices: a unified theory—0
Dimensionality Reduction with Subspace Structure Preservation—0
Dimension Estimation Using Autoencoders—0
Dimension Estimation Using Random Connection Models—0
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems—0
Show:102550
← PrevPage 317 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