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 591–600 of 3304 papers

TitleStatusHype
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds—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
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Simple, unified analysis of Johnson-Lindenstrauss with applications—0
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting MachineCode0
Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification—0
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation—0
Multilinear Kernel Regression and Imputation via Manifold Learning—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