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 1301–1310 of 3304 papers

TitleStatusHype
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
Feature Space Sketching for Logistic Regression—0
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—0
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data—0
Privacy-Preserving Federated Deep Clustering based on GAN—0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis—0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—0
Federated Multilinear Principal Component Analysis with Applications in Prognostics—0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression—0
Formation-Controlled Dimensionality Reduction—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