SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1771–1780 of 3304 papers

TitleStatusHype
Joint Dimensionality Reduction for Separable Embedding Estimation—0
Entangled Kernels -- Beyond Separability—0
Physics-aware, probabilistic model order reduction with guaranteed stability—0
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Towards glass-box CNNs—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Smile and Laugh Expressions Detection Based on Local Minimum Key Points—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