SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2831–2840 of 3304 papers

TitleStatusHype
Bayesian Exponential Family PCA—0
Bayesian full waveform inversion with sequential surrogate model refinement—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
Bayesian inverse regression for dimension reduction with small datasets—0
Bayesian Learning of Dynamic Multilayer Networks—0
Bayesian Learning of Parameterised Quantum Circuits—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Bayesian neural networks and dimensionality reduction—0
Bayesian nonparametric Principal Component Analysis—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