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 1941–1950 of 3304 papers

TitleStatusHype
Human brain activity for machine attention—0
Novelty Detection via Robust Variational AutoencodingCode0
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction—0
A Semiparametric Approach to Interpretable Machine Learning—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
Generalized Penalty for Circular Coordinate RepresentationCode0
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Semi-supervised deep learning for high-dimensional uncertainty quantification—0
Unsupervised Feature Selection via Multi-step Markov Transition Probability—0
Show:102550
← PrevPage 195 of 331Next →
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