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 951–960 of 3304 papers

TitleStatusHype
Dimensionality Reduction for Wasserstein Barycenter—0
Dimensionality Reduction for Tukey Regression—0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
Stochastic neighborhood embedding and the gradient flow of relative entropy—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
Automated Disease Normalization with Low Rank Approximations—0
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis—0
A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection—0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Dimensionality reduction for time series 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