SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1291–1300 of 3304 papers

TitleStatusHype
Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering—0
Thermal hand image segmentation for biometric recognition—0
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis—0
Schrödinger Risk Diversification Portfolio—0
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional ImagesCode0
Using the left Gram matrix to cluster high dimensional data—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data—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