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 2121–2130 of 3304 papers

TitleStatusHype
SRA: Fast Removal of General Multipath for ToF Sensors—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling—0
Stabilization Analysis and Mode Recognition of Kerosene Supersonic Combustion: A Deep Learning Approach Based on Res-CNN-beta-VAE—0
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps—0
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Static and Dynamic Robust PCA and Matrix Completion: A Review—0
Statistical Advantages of Oblique Randomized Decision Trees and Forests—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Statistical and computational trade-offs in estimation of sparse principal components—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