SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2441–2450 of 3304 papers

TitleStatusHype
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction—0
Near-Convex Archetypal Analysis—0
Nearest Neighbor CCP-Based Molecular Sequence Analysis—0
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
Nearly minimax robust estimator of the mean vector by iterative spectral dimension reduction—0
Near Optimal Sketching of Low-Rank Tensor Regression—0
Negative Dependence as a toolbox for machine learning : review and new developments—0
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection—0
Nested Diffusion Models Using Hierarchical Latent Priors—0
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design—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