SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1861–1870 of 3304 papers

TitleStatusHype
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction—0
Random Forest Autoencoders for Guided Representation Learning—0
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—0
Randomized Dimensionality Reduction for k-means Clustering—0
Randomized Dimension Reduction on Massive Data—0
Randomized Dimension Reduction with Statistical Guarantees—0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
Randomized Iterative Algorithms for Fisher Discriminant Analysis—0
A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear 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