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 2531–2540 of 3304 papers

TitleStatusHype
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Adaptive Locally Linear Embedding—0
Adaptive Metric Dimensionality Reduction—0
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Adaptive Randomized Dimension Reduction on Massive Data—0
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing—0
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery—0
A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction—0
A Data Quarantine Model to Secure Data in Edge Computing—0
Show:102550
← PrevPage 254 of 331Next →
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