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 981–990 of 3304 papers

TitleStatusHype
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders—0
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Automated Anomaly Detection on European XFEL Klystrons—0
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography—0
Discovery of sustainable energy materials via the machine-learned material space—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—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