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 1031–1040 of 3304 papers

TitleStatusHype
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
Kernel PCA for multivariate extremes—0
EVNet: An Explainable Deep Network for Dimension ReductionCode1
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant AnalysisCode0
A Generalized EigenGame with Extensions to Multiview Representation LearningCode0
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
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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