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 1651–1660 of 3304 papers

TitleStatusHype
Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning—0
Visualizing Representations of Adversarially Perturbed InputsCode0
An Impossibility Theorem for Node Embedding—0
Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra—0
Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets—0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality—0
A Clustering Framework for Residential Electric Demand ProfilesCode0
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data—0
Brain Inspired Face Recognition: A Computational Framework—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