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 541–550 of 3304 papers

TitleStatusHype
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Classification with Repulsion Tensors: A Case Study on Face Recognition—0
Show:102550
← PrevPage 55 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