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 1441–1450 of 3304 papers

TitleStatusHype
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Golden Ratio-Based Sufficient Dimension Reduction—0
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Graceful Forgetting II. Data as a Process—0
Gradient-based kernel method for feature extraction and variable selection—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
Fourier analysis perspective for sufficient dimension reduction problem—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—0
Show:102550
← PrevPage 145 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