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 911–920 of 3304 papers

TitleStatusHype
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
Dimensionality reduction for time series data—0
Dimensionality Reduction for Tukey Regression—0
Dimensionality Reduction for Wasserstein Barycenter—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
Classic machine learning methods—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