SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 861–870 of 3304 papers

TitleStatusHype
Bayesian nonparametric Principal Component Analysis—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
A dimensionality reduction technique based on the Gromov-Wasserstein distance—0
Bayesian neural networks and dimensionality reduction—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Bayesian Learning of Parameterised Quantum Circuits—0
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
A cognitive based Intrusion detection system—0
Bayesian Learning of Dynamic Multilayer 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