SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 391–400 of 3304 papers

TitleStatusHype
Robust spectral clustering with rank statistics—0
OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data—0
Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment AnalysisCode0
"Normalized Stress" is Not Normalized: How to Interpret Stress CorrectlyCode0
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling—0
Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines—0
Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics—0
Towards aerodynamic surrogate modeling based on β-variational autoencodersCode0
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions—0
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds—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