SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2461–2470 of 3304 papers

TitleStatusHype
Neuroscience-inspired online unsupervised learning algorithms—0
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance—0
News2vec: News Network Embedding with Subnode Information—0
New Transfer Learning Techniques for Disparate Label Sets—0
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images—0
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection—0
Noise-robust latent vector reconstruction in ptychography using deep generative models—0
Noisy Data Visualization using Functional Data Analysis—0
Noisy multi-label semi-supervised dimensionality reduction—0
NOMAD Projection—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