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 2661–2670 of 3304 papers

TitleStatusHype
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
A Neural Network Transformer Model for Composite Microstructure Homogenization—0
A Neural Operator-Based Emulator for Regional Shallow Water Dynamics—0
A New Approach for Scalable Analysis of Microbial Communities—0
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic—0
A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification—0
A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problems—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information—0
Show:102550
← PrevPage 267 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