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 2401–2410 of 3304 papers

TitleStatusHype
Multi-Frequency Vector Diffusion Maps—0
Multi-Label Prediction via Sparse Infinite CCA—0
Multilayer bootstrap networks—0
Multi-Linear Kernel Regression and Imputation in Data Manifolds—0
Multilinear Kernel Regression and Imputation via Manifold Learning—0
Multilingual Summarization: Dimensionality Reduction and a Step Towards Optimal Term Coverage—0
Multi Loci Phylogenetic Analysis with Gene Tree Clustering—0
Multimodal and multicontrast image fusion via deep generative models—0
Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics—0
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data—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