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 1121–1130 of 3304 papers

TitleStatusHype
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Empirical Evaluation of Kernel PCA Approximation Methods in Classification Tasks—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data—0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
Efficient Optimization for Discriminative Latent Class Models—0
Empowering individual trait prediction using interactions—0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images—0
Show:102550
← PrevPage 113 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