SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2171–2180 of 3304 papers

TitleStatusHype
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
Supervised Visualization for Data Exploration—0
On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods—0
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning—0
Surrogate to Poincaré inequalities on manifolds for dimension reduction in nonlinear feature spaces—0
Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI—0
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer—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