SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2101–2110 of 3304 papers

TitleStatusHype
Interacting with Massive Behavioral Data—0
Interactions between Representation Learning and Supervision—0
Interactive dimensionality reduction using similarity projections—0
Interactive Distillation of Large Single-Topic Corpora of Scientific Papers—0
Interactive Graphics for Visually Diagnosing Forest Classifiers in R—0
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein—0
Interpretability Beyond Classification Output: Semantic Bottleneck Networks—0
Interpretability Illusions in the Generalization of Simplified Models—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—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