SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1711–1720 of 3304 papers

TitleStatusHype
Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm—0
Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders—0
Enhancing UAV Path Planning Efficiency Through Accelerated Learning—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Ensembles of Classifiers based on Dimensionality Reduction—0
Entangled Kernels -- Beyond Separability—0
Entangled Mean Estimation in High-Dimensions—0
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes—0
Error Metrics for Learning Reliable Manifolds from Streaming Data—0
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare—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