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 2181–2190 of 3304 papers

TitleStatusHype
Latent Diffusion Model for Generating Ensembles of Climate Simulations—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
Latent Dynamic Networked System Identification with High-Dimensional Networked Data—0
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
Latent Fisher Discriminant Analysis—0
Latent Gaussian process with composite likelihoods and numerical quadrature—0
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck—0
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification—0
Latent Space Representation of Electricity Market Curves for Improved Prediction Efficiency—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