SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1331–1340 of 3304 papers

TitleStatusHype
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images—0
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification—0
Contrastive Multivariate Singular Spectrum Analysis—0
Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents—0
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex—0
Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization—0
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA—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