SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 611–620 of 3304 papers

TitleStatusHype
Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics—0
Towards aerodynamic surrogate modeling based on β-variational autoencodersCode0
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions—0
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds—0
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN—0
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image SegmentationCode0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Principal component analysis balancing prediction and approximation accuracy for spatial dataCode0
Feature Clock: High-Dimensional Effects in Two-Dimensional Plots—0
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models—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