SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2551–2560 of 3304 papers

TitleStatusHype
A description length approach to determining the number of k-means clusters—0
A determinantal point process for column subset selection—0
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
A dimensionality reduction technique based on the Gromov-Wasserstein distance—0
A Discussion On the Validity of Manifold Learning—0
A DNA Methylation Classification Model Predicts Organ and Disease Site—0
ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms—0
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN—0
Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity—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