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 1021–1030 of 3304 papers

TitleStatusHype
Distribution-based Label Space Transformation for Multi-label Learning—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Autoencoding topology—0
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs—0
A Deep Graph Embedding Network Model for Face Recognition—0
Dimensionality Reduction for Categorical Data—0
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Dimensionality reduction for acoustic vehicle classification with spectral embedding—0
Likelihood Contribution based Multi-scale Architecture for Generative Flows—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