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 1301–1310 of 3304 papers

TitleStatusHype
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in PipesCode0
Fuzzy Pooling—0
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data—0
Visualising Multiplayer Game Spaces—0
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty—0
Measuring disentangled generative spatio-temporal representationCode0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—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