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
Classification with Repulsion Tensors: A Case Study on Face Recognition—0
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed SystemsCode0
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements—0
An Innovative Imputation and Classification Approach for Accurate Disease Prediction—0
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training—0
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
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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