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 1991–2000 of 3304 papers

TitleStatusHype
Generalized Penalty for Circular Coordinate RepresentationCode0
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation—0
Semi-supervised deep learning for high-dimensional uncertainty quantification—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Unsupervised Feature Selection via Multi-step Markov Transition Probability—0
Semi-supervised Embedding Learning for High-dimensional Bayesian OptimizationCode0
QEBA: Query-Efficient Boundary-Based Blackbox Attack—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images—0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
Show:102550
← PrevPage 200 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