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 2201–2210 of 3304 papers

TitleStatusHype
Learning Clustered Representation for Complex Free Energy Landscapes—0
Learning Collective Behaviors from Observation—0
Learning Deep Representations By Distributed Random Samplings—0
Learning Deep Representation Without Parameter Inference for Nonlinear Dimensionality Reduction—0
Learning Densities Conditional on Many Interacting Features—0
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows—0
Learning Entropic Wasserstein Embeddings—0
Learning Environment Models with Continuous Stochastic Dynamics—0
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images—0
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering—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