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 1421–1430 of 3304 papers

TitleStatusHype
Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering—0
Thermal hand image segmentation for biometric recognition—0
Schrödinger Risk Diversification Portfolio—0
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis—0
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
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning—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