SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1001–1010 of 3304 papers

TitleStatusHype
Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement—0
Automated Anomaly Detection on European XFEL Klystrons—0
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
A Class-Based Agreement Model for Generating Accurately Inflected Translations—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
Dimensionality Reduction for k-means Clustering—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