SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2031–2040 of 3304 papers

TitleStatusHype
Shamap: Shape-based Manifold Learning—0
SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion—0
Shape-informed surrogate models based on signed distance function domain encoding—0
Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence—0
ShapeVis: High-dimensional Data Visualization at Scale—0
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Shining light on data: Geometric data analysis through quantum dynamics—0
SHOE: Supervised Hashing with Output Embeddings—0
Siamese networks for Poincaré embeddings and the reconstruction of evolutionary trees—0
Siamese Neural Networks for Wireless Positioning and Channel Charting—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