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 971–980 of 3304 papers

TitleStatusHype
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps—0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI—0
Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches—0
Learning Topology-Preserving Data RepresentationsCode1
Master's Thesis: Out-of-distribution Detection with Energy-based ModelsCode0
SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality ReductionCode0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
A predictive physics-aware hybrid reduced order model for reacting 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