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 3131–3140 of 3304 papers

TitleStatusHype
Dimensionality Reduction for Categorical Data—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Dimensionality Reduction for General KDE Mode Finding—0
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation—0
Dimensionality Reduction for k-means Clustering—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—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