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 411–420 of 3304 papers

TitleStatusHype
Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methodsCode0
Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds—0
Minimax-Optimal Dimension-Reduced Clustering for High-Dimensional Nonspherical Mixtures—0
Shuttle Between the Instructions and the Parameters of Large Language Models—0
Displacement-Sparse Neural Optimal Transport—0
Physically Interpretable Representation and Controlled Generation for Turbulence Data—0
Principal Components for Neural Network InitializationCode0
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality ReductionCode0
DeepFRC: An End-to-End Deep Learning Model for Functional Registration and ClassificationCode0
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre—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