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 2181–2190 of 3304 papers

TitleStatusHype
System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring—0
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration—0
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
Taming Wild High Dimensional Text Data with a Fuzzy Lash—0
On Computational Modeling of Sleep-Wake Cycle—0
TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach—0
TDAsweep: A Novel Dimensionality Reduction Method for Image Classification Tasks—0
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection—0
Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems—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