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 581–590 of 3304 papers

TitleStatusHype
Comparing Similarity Measures for Distributional Thesauri—0
Comprehensive OOD Detection Improvements—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
A note on concentration inequality for vector-valued martingales with weak exponential-type tails—0
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—0
A non-parametric conditional factor regression model for high-dimensional input and response—0
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis—0
A convex formulation for high-dimensional sparse sliced inverse regression—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