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 1501–1510 of 3304 papers

TitleStatusHype
Efficient Fair Principal Component Analysis—0
Jointly Modeling and Clustering Tensors in High Dimensions—0
Heteroscedastic Max-Min Distance Analysis—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
High-Order Low-Rank Tensors for Semantic Role Labeling—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