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 791–800 of 3304 papers

TitleStatusHype
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
You can monitor your hydration level using your smartphone camera—0
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment—0
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting MachineCode0
Simple, unified analysis of Johnson-Lindenstrauss with applications—0
Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification—0
Multilinear Kernel Regression and Imputation via Manifold Learning—0
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation—0
On Minimum Trace Factor Analysis - An Old Song Sung to a New Tune—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