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 2061–2070 of 3304 papers

TitleStatusHype
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization—0
Smart Analytical Signature Verification For DSP Applications—0
Smile and Laugh Expressions Detection Based on Local Minimum Key Points—0
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo—0
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition—0
Solve sparse PCA problem by employing Hamiltonian system and leapfrog method—0
Solving Interpretable Kernel Dimensionality Reduction—0
Some Options for L1-Subspace Signal Processing—0
SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing—0
Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting—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