SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1511–1520 of 3304 papers

TitleStatusHype
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering—0
Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm—0
Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries—0
Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets—0
Fitting a Simplicial Complex using a Variation of k-means—0
Computing Gram Matrix for SMILES Strings using RDKFingerprint and Sinkhorn-Knopp Algorithm—0
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
Computing Approximate _p Sensitivities—0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—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