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 2531–2540 of 3304 papers

TitleStatusHype
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation—0
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres—0
On metric choice in dimension reduction for Fréchet regression—0
On Minimum Trace Factor Analysis - An Old Song Sung to a New Tune—0
On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding—0
On Optimality in ROVir—0
On Principal Components Regression, Random Projections, and Column Subsampling—0
On relative universality, regression operator, and conditional independence—0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
On Sufficient Graphical Models—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