SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2211–2220 of 3304 papers

TitleStatusHype
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction—0
The face-space duality hypothesis: a computational model—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
The G-invariant graph Laplacian—0
The Information Bottleneck Problem and Its Applications in Machine Learning—0
The Informativeness of K -Means for Learning Mixture Models—0
The intrinsic value of HFO features as a biomarker of epileptic activity—0
The Mathematical Foundations of Manifold Learning—0
The Mathematics Behind Spectral Clustering And The Equivalence To PCA—0
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation—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