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 1201–1210 of 3304 papers

TitleStatusHype
Kernel PCA for multivariate extremes—0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
A Generalized EigenGame with Extensions to Multiview Representation LearningCode0
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant AnalysisCode0
Graceful Forgetting II. Data as a Process—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—0
Data Dimension Reduction makes ML Algorithms efficient—0
Topology of cognitive maps—0
Inverse Kernel DecompositionCode0
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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