SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 921–930 of 3304 papers

TitleStatusHype
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Dimensionality reduction methods for molecular simulations—0
Dimensionality Reduction of Affine Variational Inequalities Using Random Projections—0
Dimensionality Reduction of Collective Motion by Principal Manifolds—0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Dimensionality Reduction of Massive Sparse Datasets Using Coresets—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Classic machine learning methods—0
An Impossibility Theorem for Node Embedding—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