SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 3151–3160 of 3304 papers

TitleStatusHype
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Dimensionality Reduction of Massive Sparse Datasets Using Coresets—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Dimensionality reduction techniques to support insider trading detection—0
Dimensionality reduction: theoretical perspective on practical measures—0
Dimensionality Reduction using Elastic Measures—0
Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis—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