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 1791–1800 of 3304 papers

TitleStatusHype
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer—0
Geometry-Aware Hamiltonian Variational Auto-Encoder—0
Tensor Train Random Projection—0
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Sufficient dimension reduction for classification using principal optimal transport directionCode0
Product Manifold LearningCode0
Statistical guarantees for generative models without domination—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