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 241–250 of 3304 papers

TitleStatusHype
CSI Compression using Channel Charting—0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
Representation learning of dynamic networks—0
Alternative Channel Charting Techniques in Cellular Wireless Communications—0
Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities—0
A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition—0
Show:102550
← PrevPage 25 of 331Next →
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