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 1701–1710 of 3304 papers

TitleStatusHype
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions—0
On the Power of SVD in the Stochastic Block Model—0
On the reduction of Linear Parameter-Varying State-Space models—0
On The Relative Error of Random Fourier Features for Preserving Kernel Distance—0
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System—0
On the Robustness of CountSketch to Adaptive Inputs—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
On the Use of the Kantorovich-Rubinstein Distance for Dimensionality Reduction—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