SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 131–140 of 3304 papers

TitleStatusHype
HUMAP: Hierarchical Uniform Manifold Approximation and ProjectionCode1
Unsupervised Behaviour Discovery with Quality-Diversity OptimisationCode1
Large-scale optimal transport map estimation using projection pursuitCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate CurvatureCode1
WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise LabelsCode1
Estimating leverage scores via rank revealing methods and randomizationCode1
A hyperparameter-tuning approach to automated inverse planningCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
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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