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 1981–1990 of 3304 papers

TitleStatusHype
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering—0
Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries—0
Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets—0
Hierarchical Trait-State Model for Decoding Dyadic Social Interactions—0
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—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