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 2941–2950 of 3304 papers

TitleStatusHype
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training—0
XGBoost: A Scalable Tree Boosting SystemCode4
Learning a Discriminative Null Space for Person Re-identification—0
Whitening-Free Least-Squares Non-Gaussian Component AnalysisCode0
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space—0
Ordonnancement d'entités pour la rencontre du web des documents et du web des données—0
Joint Dimensionality Reduction for Two Feature Vectors—0
Semi-supervised Learning with Explicit Relationship Regularization—0
Local High-order Regularization on Data Manifolds—0
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting—0
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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