SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1361–1370 of 3304 papers

TitleStatusHype
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries—0
Contextual Bandits with Sparse Data in Web setting—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Efficiently Computing Similarities to Private Datasets—0
Contextual Categorization Enhancement through LLMs Latent-Space—0
Functional Inverse Regression in an Enlarged Dimension Reduction Space—0
Efficient Learning and Planning with Compressed Predictive States—0
Functional sufficient dimension reduction through information maximization with application to classification—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—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