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 2661–2670 of 3304 papers

TitleStatusHype
Principal Component Analysis When n < p: Challenges and Solutions—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams)—0
Principal Model Analysis Based on Partial Least Squares—0
Principal Polynomial Analysis—0
Principal subbundles for dimension reduction—0
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Privacy-Enhancing Context Authentication from Location-Sensitive Data—0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation—0
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data—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