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 761–770 of 3304 papers

TitleStatusHype
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis—0
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation—0
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck—0
Predicting Fatigue Crack Growth via Path Slicing and Re-WeightingCode0
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles—0
BDEC:Brain Deep Embedded Clustering model—0
Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension—0
Robust Nonlinear Reduced-Order Model Predictive Control—0
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck—0
Non-linear dimension reduction in factor-augmented vector autoregressions—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