SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1401–1410 of 3304 papers

TitleStatusHype
Semi-Supervised Graph Learning Meets Dimensionality ReductionCode0
Evolution is Driven by Natural Autoencoding: Reframing Species, Interaction Codes, Cooperation, and Sexual Reproduction—0
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection—0
Dimensionality Reduction and Wasserstein Stability for Kernel Regression—0
Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients—0
Sparse random hypergraphs: Non-backtracking spectra and community detectionCode0
Dimensionality Reduction and Prioritized Exploration for Policy Search—0
Bayesian Calibration for Activity Based Models—0
Diffusion Maps : Using the Semigroup Property for Parameter Tuning—0
Uniform Approximations for Randomized Hadamard Transforms with Applications—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