SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2091–2100 of 3304 papers

TitleStatusHype
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
An automated approach for task evaluation using EEG signals—0
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity—0
News2vec: News Network Embedding with Subnode Information—0
Privacy-Preserving Machine Learning Using EtC Images—0
Solving NMF with smoothness and sparsity constraints using PALMCode0
A Comparison of Neural Network Training Methods for Text Classification—0
Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies—0
Variational Quantum Algorithms for Dimensionality Reduction and Classification—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