SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1201–1210 of 3304 papers

TitleStatusHype
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
Deep-gKnock: nonlinear group-feature selection with deep neural network—0
A Semiparametric Approach to Interpretable Machine Learning—0
Algorithm-Agnostic Interpretations for Clustering—0
Adaptive Metric Dimensionality Reduction—0
Accelerating hyperbolic t-SNE—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
A selective review of sufficient dimension reduction for multivariate response regression—0
Deep Gaussian Mixture Models—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—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