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 1161–1170 of 3304 papers

TitleStatusHype
Extração e Classificação de Características Radiômicas em Gliomas de Baixo Grau para Análise da Codeleção 1p/19q—0
Error Metrics for Learning Reliable Manifolds from Streaming Data—0
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare—0
ESPACE: Dimensionality Reduction of Activations for Model Compression—0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
Estimating a Manifold from a Tangent Bundle Learner—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Estimating covariance and precision matrices along subspaces—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf Optimizer (GWO) for text feature selection and clustering—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